Object Detection Evaluation 2012


The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

  • Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
  • Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
  • Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • Additional training data: Use of additional data sources for training (see details)

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 THU CV-AI 91.97 % 91.96 % 84.57 % 0.38 s GPU @ 2.5 Ghz (Python)
2 DH-ARI 91.48 % 90.87 % 82.25 % 4s GPU @ 2.5 Ghz (C/C++)
3 HRI-SH 90.71 % 91.34 % 84.28 % 3.6 s GPU @ >3.5 Ghz (Python + C/C++)
4 BM-NET 90.50 % 90.81 % 83.92 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
5 MVRA + I-FRCNN+ 90.36 % 90.78 % 80.48 % 0.18 s GPU @ 2.5 Ghz (Python)
6 TuSimple code 90.33 % 90.77 % 82.86 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
7 RRC code 90.23 % 90.61 % 87.44 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
8 CFENet 90.22 % 90.33 % 84.85 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
9 MMF
This method makes use of Velodyne laser scans.
90.17 % 91.82 % 88.54 % 0.08 s GPU @ 2.5 Ghz (Python)
10 EM-FPS 90.15 % 90.61 % 84.01 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
11 icst-SSD 90.08 % 90.30 % 84.70 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
12 SJTU-HW 90.08 % 90.81 % 79.98 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
13 MDC
This method makes use of Velodyne laser scans.
90.03 % 90.72 % 80.87 % 0.2 s volta v100
14 Deep MANTA 90.03 % 97.25 % 80.62 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
15 sensekitti code 90.00 % 90.76 % 81.83 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
16 F-PointNet
This method makes use of Velodyne laser scans.
code 90.00 % 90.78 % 80.80 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
17 ECV-NET 89.93 % 90.61 % 81.81 % 0.4 s GPU @ 2.5 Ghz (C/C++)
18 ART-Det 89.89 % 95.18 % 80.03 % 0.067s GPU @ 2.5 Ghz (Python + C/C++)
19 FNV2 89.88 % 90.51 % 80.66 % 0.18 s GPU @ 2.5 Ghz (Python)
20 MBR-SSD 89.82 % 90.32 % 82.28 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
21 PointRCNN
This method makes use of Velodyne laser scans.
89.75 % 90.77 % 80.98 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
22 SINet+ code 89.73 % 90.51 % 77.82 % 0.3 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2018.
23 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
89.69 % 90.59 % 80.83 % 0.2 s GPU @ >3.5 Ghz (Python)
24 AILabs3D
This method makes use of Velodyne laser scans.
89.68 % 90.57 % 80.67 % 0.6 s GPU @ >3.5 Ghz (Python)
25 Paul-Fr-RCNN 89.59 % 90.76 % 77.23 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
26 D3D
This method makes use of Velodyne laser scans.
89.59 % 90.51 % 80.57 % 0.4 s 1 core @ 3.5 Ghz (Python)
27 SINet_VGG code 89.56 % 90.60 % 78.19 % 0.2 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2018.
28 Fast Point R-CNN
This method makes use of Velodyne laser scans.
89.51 % 90.58 % 87.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
29 DH-ARI 89.47 % 90.31 % 84.78 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
30 SDP+RPN 89.42 % 89.90 % 78.54 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
31 VAT-Net 89.41 % 90.69 % 79.97 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
32 IPOD 89.30 % 90.20 % 87.37 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. arXiv preprint arXiv:1812.05276 2018.
33 MVX-Net
This method makes use of Velodyne laser scans.
89.25 % 90.43 % 80.47 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
34 ITVD code 89.23 % 90.57 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
35 PointPillars
This method makes use of Velodyne laser scans.
89.22 % 90.33 % 87.04 % 16 ms 1080ti GPU and Intel i7 CPU
36 CONV-BOX
This method makes use of Velodyne laser scans.
89.20 % 90.35 % 87.88 % 0.2 s Tesla V100
37 VCTNet 89.20 % 89.60 % 80.04 % 0.02 s GPU @ 1.5 Ghz (C/C++)
38 Sogo_MM 89.17 % 90.80 % 79.58 % 1.5 s GPU @ 2.5 Ghz (C/C++)
39 MV3D
This method makes use of Velodyne laser scans.
89.17 % 90.53 % 80.16 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
40 SINet_PVA code 89.08 % 90.44 % 75.85 % 0.11 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2018.
41 LTT
This method makes use of Velodyne laser scans.
89.00 % 90.16 % 81.94 % 0.4 s 1 core @ 3.5 Ghz (Python)
42 CLA 88.99 % 90.51 % 75.50 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
43 InNet 88.95 % 90.26 % 79.46 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
44 SubCNN 88.86 % 90.75 % 79.24 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
45 Deep3DBox 88.86 % 90.47 % 77.60 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
46 desNet 88.85 % 90.51 % 79.36 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
47 MonoPSR 88.84 % 90.18 % 71.44 % 0.2 s GPU @ 3.5 Ghz (Python)
48 MS-CNN code 88.83 % 90.46 % 74.76 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
49 RoarNet
This method makes use of Velodyne laser scans.
code 88.80 % 90.69 % 79.46 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin, Y. Kwon and M. Tomizuka: RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement. arXiv preprint arXiv:1811.03818 2018.
50 vfnet 88.77 % 89.63 % 79.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
51 RCN-resnet101 88.75 % 89.08 % 79.97 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
52 DeepStereoOP 88.75 % 90.34 % 79.39 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
53 SAG-Net 88.61 % 89.25 % 79.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
54 ARPNET 88.44 % 90.26 % 79.86 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
55 SECOND code 88.40 % 90.40 % 80.21 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
56 3DOP
This method uses stereo information.
code 88.34 % 90.09 % 78.79 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
57 DA 88.23 % 90.45 % 74.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
58 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.20 % 90.93 % 78.02 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
59 AVOD
This method makes use of Velodyne laser scans.
code 88.08 % 89.73 % 80.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
60 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
87.86 % 90.02 % 79.95 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
61 Mono3D code 87.86 % 90.27 % 78.09 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
62 merge12-12 87.81 % 89.88 % 77.42 % 0.2 s 4 cores @ 2.5 Ghz (Python)
63 DFD 87.78 % 90.02 % 79.78 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
64 AVOD-FPN
This method makes use of Velodyne laser scans.
code 87.44 % 89.99 % 80.05 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
65 SECA 87.42 % 89.57 % 79.43 % 0.09 s GPU @ 2.5 Ghz (Python)
66 SCANet 87.31 % 89.34 % 79.30 % 0.09s GPU @ 2.5 Ghz (Python)
67 CNN-ds code 87.15 % 86.86 % 70.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
68 ODES code 87.10 % 86.82 % 78.32 % 0.02 s GPU @ 2.5 Ghz (Python)
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69 ELLIOT
This method makes use of Velodyne laser scans.
86.98 % 90.20 % 81.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 cascadercnn 85.86 % 84.21 % 69.57 % 0.36 s 4 cores @ 2.5 Ghz (Python)
71 ReSqueeze 85.74 % 87.12 % 77.02 % 0.03 s GPU @ >3.5 Ghz (Python)
72 anm 85.33 % 90.11 % 76.55 % 3 s 1 core @ 2.5 Ghz (C/C++)
73 YOLOv3+d 84.13 % 84.30 % 76.34 % 0.04 s GPU @ 1.5 Ghz (C/C++)
74 LPN 81.67 % 87.70 % 72.69 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
75 A3DODWTDA (image) code 81.54 % 76.21 % 66.85 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
76 SDP+CRC (ft) 81.33 % 90.39 % 70.33 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
77 ResNet-RRC w/RGBD 81.09 % 89.91 % 71.78 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
78 Stereo R-CNN
This method uses stereo information.
81.04 % 90.53 % 71.71 % 0.4 s GPU @ 2.5 Ghz (Python)
79 ResNet-RRC (Adv. HW) 81.00 % 89.89 % 71.56 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
80 X_MD 80.65 % 89.81 % 79.66 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
81 FNV1_Fusion 80.41 % 89.37 % 79.03 % 0.11 s GPU @ 2.5 Ghz (Python)
82 FNV1_RPN 80.41 % 89.44 % 79.14 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
83 Cmerge 80.25 % 89.83 % 70.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
84 SS3D 80.17 % 89.31 % 70.51 % 88 ms Tesla V100 (Python)
85 VSE 80.05 % 89.26 % 78.80 % 0.15 s GPU @ 2.5 Ghz (Python)
86 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
79.76 % 89.80 % 78.61 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
87 ZKNet 79.62 % 89.89 % 70.03 % 0.01 s GPU @ 2.0 Ghz (Python)
88 RFCN_RFB 79.45 % 83.85 % 67.51 % 0.2 s 4 cores @ 2.5 Ghz (Python)
89 Complexer-YOLO
This method makes use of Velodyne laser scans.
79.31 % 88.11 % 79.11 % 0.06 s GPU @ 3.5 Ghz (C/C++)
90 FNV1 79.28 % 88.45 % 77.14 % 0.11 s GPU @ 2.5 Ghz (Python)
91 RefineNet 79.21 % 90.16 % 65.71 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
92 retinanetkitti 79.18 % 85.90 % 70.04 % 1.5 s 1 core @ 2.5 Ghz (Python)
93 softretina 79.15 % 89.36 % 69.24 % 0.16 s 4 cores @ 2.5 Ghz (Python)
94 Faster R-CNN code 79.11 % 87.90 % 70.19 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
95 Kiwoo 79.06 % 89.23 % 70.50 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
96 detectron code 78.96 % 88.14 % 69.74 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
97 FRCNN+Or code 78.95 % 89.87 % 68.97 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
98 Resnet101Faster rcnn 78.93 % 87.97 % 69.80 % 1 s 1 core @ 2.5 Ghz (Python)
99 Retinanet100 78.85 % 89.83 % 68.73 % 0.2 s 4 cores @ 2.5 Ghz (Python)
100 SeRC 78.33 % 88.28 % 69.36 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
101 T2Method 78.26 % 88.55 % 69.76 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
102 avodC 77.54 % 86.86 % 70.00 % 0.1 s GPU @ 2.5 Ghz (Python)
103 spLBP 77.39 % 80.16 % 60.59 % 1.5 s 8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
104 SceneNet 77.34 % 87.90 % 68.38 % 0.03 s GPU @ 2.5 Ghz (C/C++)
105 CLF3D
This method makes use of Velodyne laser scans.
77.00 % 84.51 % 67.81 % 0.13 s GPU @ 2.5 Ghz (Python)
106 MTDP 76.91 % 84.24 % 67.91 % 0.15 s GPU @ 2.0 Ghz (Python)
107 NLK 76.81 % 85.26 % 74.78 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
108 Reinspect code 76.65 % 88.36 % 66.56 % 2s 1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
109 Regionlets 76.56 % 86.50 % 59.82 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
110 AOG code 75.97 % 85.58 % 60.96 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
111 GS3D 75.84 % 83.92 % 60.24 % 0.58 s GPU @ 2.5 Ghz (C/C++)
112 3D FCN
This method makes use of Velodyne laser scans.
75.83 % 85.54 % 68.30 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
113 3D-SSMFCNN code 75.78 % 75.51 % 67.75 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
114 3DVP code 75.77 % 81.46 % 65.38 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
115 Pose-RCNN 75.74 % 88.89 % 61.86 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
116 SubCat code 75.46 % 81.45 % 59.71 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
117 DimStr-LKY 75.22 % 81.21 % 67.28 % 0.1 s GPU @ 2.5 Ghz (Matlab + C/C++)
118 multi-task CNN 75.21 % 83.45 % 66.89 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
119 A3DODWTDA
This method makes use of Velodyne laser scans.
code 74.71 % 78.21 % 66.70 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
120 FD2 74.68 % 87.14 % 65.70 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
121 BdCost+ 74.07 % 83.02 % 59.06 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
122 RFCN 73.56 % 80.70 % 61.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
123 tiny-det 73.46 % 81.88 % 63.70 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
124 3DVSSD 73.39 % 84.39 % 65.64 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
125 bin 73.31 % 76.05 % 63.76 % 15ms s GPU @ >3.5 Ghz (Python)
126 yolo800 73.00 % 76.45 % 64.68 % 0.13 s 4 cores @ 2.5 Ghz (Python)
127 ResNet-RRC (Noised) 71.81 % 78.97 % 63.57 % .057 s GPU @ 1.5 Ghz (Python + C/C++)
128 MV-RGBD-RF
This method makes use of Velodyne laser scans.
69.92 % 76.49 % 57.47 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
129 AOG-View 69.89 % 84.29 % 57.25 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
130 ROI-10D 69.64 % 75.33 % 61.18 % 0.2 s GPU @ 3.5 Ghz (Python)
131 fasterrcnn 68.76 % 73.64 % 59.72 % 0.2 s 4 cores @ 2.5 Ghz (Python)
132 MF3D 68.72 % 88.46 % 58.70 % 0.03 s GPU @ 2.5 Ghz (C/C++)
133 Vote3Deep
This method makes use of Velodyne laser scans.
68.39 % 76.95 % 63.22 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
134 PL
This method uses stereo information.
67.96 % 85.08 % 59.55 % 0.4 s GPU @ 2.5 Ghz (C/C++)
135 GPVL 67.89 % 77.76 % 58.23 % 10 s 1 core @ 2.5 Ghz (C/C++)
136 RFBnet 67.86 % 82.31 % 59.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
137 Fast-SSD 67.17 % 83.89 % 59.09 % 0.06 s GTX650Ti
138 BdCost48LDCF code 67.08 % 77.93 % 51.15 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
139 VoxelNet(Unofficial) 66.77 % 65.64 % 60.74 % 0.5 s GPU @ 2.0 Ghz (Python)
140 OC-DPM 66.45 % 76.16 % 53.70 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
141 DPM-VOC+VP 66.25 % 80.45 % 49.86 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
142 BdCost48-25C 65.95 % 78.21 % 51.23 % 4 s 1 core @ 2.5 Ghz (C/C++)
143 PL
This method uses stereo information.
65.93 % 78.51 % 57.77 % 0.4 s GPU @ 2.5 Ghz (Python)
144 MDPM-un-BB 64.20 % 77.32 % 50.18 % 60 s 4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
145 PDV-Subcat 63.15 % 77.33 % 49.75 % 7 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
146 Lidar_ROI+Yolo(UJS) 62.71 % 70.58 % 55.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
147 GNN 62.59 % 76.03 % 50.18 % 0.2 s 1 core @ 2.5 Ghz (Python)
148 DPM-C8B1
This method uses stereo information.
60.99 % 74.95 % 47.16 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
149 SubCat48LDCF code 60.53 % 78.16 % 43.66 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
150 Fast-SSD 60.24 % 83.39 % 51.96 % 0.06 s GTX650Ti
151 SA-3D
This method makes use of Velodyne laser scans.
59.14 % 69.84 % 49.62 % 0.3 s 1 core @ 2.5 Ghz (Python)
152 SAMME48LDCF code 58.50 % 76.22 % 47.50 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
153 BirdNet
This method makes use of Velodyne laser scans.
57.47 % 78.18 % 56.66 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
154 100Frcnn 57.47 % 81.09 % 48.37 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
155 LSVM-MDPM-sv 57.44 % 71.70 % 46.58 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
156 LSVM-MDPM-us code 56.10 % 70.52 % 42.87 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
157 ACF-SC 55.76 % 69.76 % 46.27 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
158 VeloFCN
This method makes use of Velodyne laser scans.
53.45 % 70.68 % 46.90 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
159 ACF 52.81 % 62.82 % 43.89 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
160 TopNet-HighRes
This method makes use of Velodyne laser scans.
48.87 % 59.77 % 43.15 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
161 Vote3D
This method makes use of Velodyne laser scans.
48.05 % 56.66 % 42.64 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
162 Multimodal Detection
This method makes use of Velodyne laser scans.
code 46.77 % 64.04 % 39.38 % 0.06 s GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
163 softyolo 45.77 % 62.82 % 39.77 % 0.16 s 4 cores @ 2.5 Ghz (Python)
164 rpn 43.99 % 65.47 % 36.33 % 0.01 s 1 core @ 2.5 Ghz (Python)
165 VoxelNet basic
This method makes use of Velodyne laser scans.
43.44 % 45.07 % 39.59 % 0.07 s GPU (Python)
166 RT3D
This method makes use of Velodyne laser scans.
39.71 % 49.96 % 41.47 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
167 KD53-20 37.82 % 52.30 % 32.71 % 0.19 s 4 cores @ 2.5 Ghz (Python)
168 DT3D 35.98 % 49.23 % 31.78 % 0,21s GPU @ 2.5 Ghz (Python)
169 KD45 34.36 % 42.94 % 30.99 % 0.16 s 4 cores @ 2.5 Ghz (Python)
170 Licar
This method makes use of Velodyne laser scans.
33.89 % 41.60 % 35.17 % 0.09 s GPU @ 2.0 Ghz (Python)
171 Kyolo3 33.01 % 47.18 % 27.57 % 0.16 s 4 cores @ 2.5 Ghz (Python)
172 CSoR
This method makes use of Velodyne laser scans.
code 26.13 % 35.24 % 22.69 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
173 R-CNN_VGG 26.04 % 32.23 % 20.93 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
174 FCN-Depth code 25.66 % 50.55 % 24.95 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
175 mBoW
This method makes use of Velodyne laser scans.
23.76 % 37.63 % 18.44 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
176 DepthCN
This method makes use of Velodyne laser scans.
code 23.21 % 37.59 % 18.00 % 2.3 s GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
177 DLnet 20.30 % 23.46 % 17.96 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
178 YOLOv2 code 19.31 % 28.37 % 15.94 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
179 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.04 % 0.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
180 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
181 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 Alibaba-CityBrain 80.90 % 88.13 % 74.08 % 1.5 s GPU @ 2.5 Ghz (Python + C/C++)
2 DH-ARI 78.29 % 87.43 % 69.91 % 3.6 s GPU @ 2.5 Ghz (Python + C/C++)
3 EM-FPS 77.61 % 84.93 % 72.52 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
4 F-PointNet
This method makes use of Velodyne laser scans.
code 77.25 % 87.81 % 74.46 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
5 TuSimple code 77.04 % 86.78 % 72.40 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
6 Argus_detection_v1 75.51 % 83.49 % 71.24 % 0.25 s GPU @ 1.5 Ghz (C/C++)
7 RRC code 75.33 % 84.16 % 70.39 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
8 VCTNet 75.22 % 85.49 % 71.55 % 0.02 s GPU @ 1.5 Ghz (C/C++)
9 ECP Faster R-CNN 74.27 % 84.12 % 70.06 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
10 SJTU-HW 74.24 % 85.42 % 69.34 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
11 CLA 74.03 % 84.26 % 68.45 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
12 ECV-NET 73.74 % 84.58 % 66.35 % 0.4 s GPU @ 2.5 Ghz (C/C++)
13 BOE_IOT_AIBD 73.73 % 84.67 % 68.71 % 0.8 s GPU @ 2.5 Ghz (Python)
14 MS-CNN code 73.62 % 83.70 % 68.28 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
15 retinanetkitti 73.40 % 82.94 % 69.04 % 1.5 s 1 core @ 2.5 Ghz (Python)
16 SAITv1 72.61 % 84.79 % 67.94 % 0.15 s GPU @ 2.5 Ghz (C/C++)
17 Sogo_MM 71.84 % 83.45 % 67.00 % 1.5 s GPU @ 2.5 Ghz (C/C++)
18 GN 71.55 % 80.73 % 64.82 % 1 s GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
19 SubCNN 71.34 % 83.17 % 66.36 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
20 IVA code 70.63 % 83.03 % 64.68 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
21 SDP+RPN 70.20 % 79.98 % 64.84 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
22 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
69.96 % 82.37 % 64.76 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
23 MDC
This method makes use of Velodyne laser scans.
69.58 % 86.37 % 68.44 % 0.2 s volta v100
24 MonoPSR 68.91 % 85.93 % 60.83 % 0.2 s GPU @ 3.5 Ghz (Python)
25 HBA-RCNN 68.26 % 77.76 % 62.86 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
26 DA 67.89 % 79.91 % 64.83 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
27 3DOP
This method uses stereo information.
code 67.46 % 82.36 % 64.71 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
28 DeepStereoOP 67.32 % 82.50 % 65.14 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
29 sensekitti code 67.28 % 80.12 % 62.25 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
30 ODES code 67.25 % 77.95 % 62.28 % 0.02 s GPU @ 2.5 Ghz (Python)
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31 Mono3D code 66.66 % 77.30 % 63.44 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
32 Faster R-CNN code 65.91 % 78.35 % 61.19 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
33 SDP+CRC (ft) 64.25 % 77.81 % 59.31 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
34 PCN 63.48 % 77.88 % 58.59 % 0.6 s
35 Pose-RCNN 63.38 % 77.69 % 57.42 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
36 CFM 63.26 % 74.21 % 56.44 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
37 IPOD 63.07 % 73.28 % 56.71 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. arXiv preprint arXiv:1812.05276 2018.
38 ALV303 61.77 % 69.13 % 54.54 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
39 RPN+BF code 61.29 % 75.58 % 56.08 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
40 ReSqueeze 61.25 % 72.78 % 57.43 % 0.03 s GPU @ >3.5 Ghz (Python)
41 Regionlets 61.16 % 72.96 % 55.22 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
42 merge12-12 60.66 % 78.15 % 58.67 % 0.2 s 4 cores @ 2.5 Ghz (Python)
43 cascadercnn 60.64 % 77.88 % 52.69 % 0.36 s 4 cores @ 2.5 Ghz (Python)
44 bin 60.54 % 70.13 % 56.55 % 15ms s GPU @ >3.5 Ghz (Python)
45 anm 59.21 % 75.51 % 56.49 % 3 s 1 core @ 2.5 Ghz (C/C++)
46 CompACT-Deep 58.73 % 69.70 % 52.69 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
47 DeepParts 58.68 % 70.46 % 52.73 % ~1 s GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
48 AVOD-FPN
This method makes use of Velodyne laser scans.
code 58.42 % 67.32 % 57.44 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
49 LPN 58.18 % 70.54 % 54.18 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
50 SA-3D
This method makes use of Velodyne laser scans.
57.40 % 68.11 % 50.09 % 0.3 s 1 core @ 2.5 Ghz (Python)
51 FilteredICF 57.12 % 69.05 % 51.46 % ~ 2 s >8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
52 ZKNet 57.11 % 70.20 % 52.15 % 0.01 s GPU @ 2.0 Ghz (Python)
53 RFCN 56.91 % 71.17 % 50.06 % 0.2 s 4 cores @ 2.5 Ghz (Python)
54 FRCNN+Or code 56.78 % 71.18 % 52.86 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
55 FD2 56.68 % 71.09 % 51.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
56 MV-RGBD-RF
This method makes use of Velodyne laser scans.
56.59 % 73.05 % 49.63 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
57 CHTTL MMF 56.01 % 73.22 % 50.26 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
58 RFCN_RFB 55.86 % 69.32 % 49.18 % 0.2 s 4 cores @ 2.5 Ghz (Python)
59 SECOND code 55.74 % 65.73 % 49.08 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
60 PointPillars
This method makes use of Velodyne laser scans.
55.68 % 64.66 % 53.93 % 16 ms 1080ti GPU and Intel i7 CPU
61 yolo800 55.49 % 71.11 % 53.92 % 0.13 s 4 cores @ 2.5 Ghz (Python)
62 Vote3Deep
This method makes use of Velodyne laser scans.
55.38 % 67.94 % 52.62 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
63 CONV-BOX
This method makes use of Velodyne laser scans.
55.23 % 63.98 % 54.18 % 0.2 s Tesla V100
64 TAFT 54.59 % 67.07 % 48.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
65 pAUCEnsT 54.58 % 66.11 % 48.49 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
66 fasterrcnn 53.80 % 69.00 % 51.35 % 0.2 s 4 cores @ 2.5 Ghz (Python)
67 PDV2 53.74 % 65.71 % 49.47 % 3.7 s 1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
68 MTDP 52.97 % 66.97 % 47.64 % 0.15 s GPU @ 2.0 Ghz (Python)
69 detectron code 52.42 % 69.89 % 51.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 YOLOv3+d 51.03 % 67.23 % 48.87 % 0.04 s GPU @ 1.5 Ghz (C/C++)
71 ACFD
This method makes use of Velodyne laser scans.
code 50.91 % 61.59 % 45.51 % 0.2 s 4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
72 CLF3D
This method makes use of Velodyne laser scans.
50.25 % 66.10 % 48.66 % 0.13 s GPU @ 2.5 Ghz (Python)
73 R-CNN 50.20 % 62.05 % 44.85 % 4 s GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
74 SeRC 49.81 % 65.31 % 42.40 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
75 Resnet101Faster rcnn 49.64 % 64.97 % 48.47 % 1 s 1 core @ 2.5 Ghz (Python)
76 ARPNET 48.77 % 63.16 % 47.65 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
77 ELLIOT
This method makes use of Velodyne laser scans.
48.26 % 59.05 % 45.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 tiny-det 47.81 % 62.02 % 45.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
79 ACF 47.29 % 60.11 % 42.90 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
80 Fusion-DPM
This method makes use of Velodyne laser scans.
code 46.67 % 59.38 % 42.05 % ~ 30 s 1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
81 ACF-MR 46.23 % 58.85 % 42.10 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
82 HA-SSVM 45.51 % 58.91 % 41.08 % 21 s 1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
83 DPM-VOC+VP 44.86 % 59.60 % 40.37 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
84 Cmerge 44.81 % 62.62 % 44.53 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 ACF-SC 44.77 % 54.20 % 39.57 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
86 SquaresICF code 44.42 % 57.47 % 40.08 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
87 SS3D 43.51 % 60.11 % 42.62 % 88 ms Tesla V100 (Python)
88 AVOD
This method makes use of Velodyne laser scans.
code 43.49 % 51.64 % 37.79 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
89 Retinanet100 42.83 % 52.43 % 35.02 % 0.2 s 4 cores @ 2.5 Ghz (Python)
90 GNN 42.56 % 58.22 % 40.53 % 0.2 s 1 core @ 2.5 Ghz (Python)
91 SubCat 42.34 % 54.06 % 37.95 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
92 softyolo 40.78 % 55.95 % 39.57 % 0.16 s 4 cores @ 2.5 Ghz (Python)
93 ACF 40.62 % 49.08 % 36.66 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
94 multi-task CNN 40.34 % 51.38 % 34.98 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
95 KD45 40.10 % 49.05 % 36.22 % 0.16 s 4 cores @ 2.5 Ghz (Python)
96 KD53-20 39.90 % 47.15 % 35.32 % 0.19 s 4 cores @ 2.5 Ghz (Python)
97 LSVM-MDPM-sv 39.36 % 51.75 % 35.95 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
98 pedestrian_cnn 39.07 % 53.60 % 37.91 % 1 s 1 core @ 2.5 Ghz (C/C++)
99 Lidar_ROI+Yolo(UJS) 38.76 % 47.11 % 32.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 LSVM-MDPM-us code 38.35 % 50.01 % 34.78 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
101 37.45 % 45.89 % 35.08 %
102 X_MD 37.38 % 50.17 % 36.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
103 anonymous
This method makes use of Velodyne laser scans.
36.65 % 49.15 % 36.18 % 0.75 s GPU @ 3.5 Ghz (C/C++)
104 NLK 36.48 % 42.71 % 34.93 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
105 Complexer-YOLO
This method makes use of Velodyne laser scans.
36.10 % 42.63 % 35.57 % 0.06 s GPU @ 3.5 Ghz (C/C++)
106 Vote3D
This method makes use of Velodyne laser scans.
35.74 % 44.47 % 33.72 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
107 rpn 32.79 % 46.95 % 31.70 % 0.01 s 1 core @ 2.5 Ghz (Python)
108 mBoW
This method makes use of Velodyne laser scans.
31.37 % 44.36 % 30.62 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
109 BirdNet
This method makes use of Velodyne laser scans.
30.90 % 36.83 % 29.93 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
110 DPM-C8B1
This method uses stereo information.
29.03 % 38.96 % 25.61 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
111 100Frcnn 26.73 % 35.65 % 26.46 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
112 R-CNN_VGG 23.16 % 28.95 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
113 Kyolo3 20.99 % 25.73 % 20.51 % 0.16 s 4 cores @ 2.5 Ghz (Python)
114 DT3D 19.19 % 27.02 % 18.98 % 0,21s GPU @ 2.5 Ghz (Python)
115 TopNet-HighRes
This method makes use of Velodyne laser scans.
17.57 % 22.98 % 17.35 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
116 Fast-SSD 16.30 % 23.14 % 16.06 % 0.06 s GTX650Ti
117 YOLOv2 code 16.19 % 20.80 % 15.43 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
118 BIP-HETERO 13.38 % 14.85 % 13.25 % ~2 s 1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
119 softretina 0.93 % 0.68 % 0.95 % 0.16 s 4 cores @ 2.5 Ghz (Python)
120 JSyolo 0.44 % 0.35 % 0.45 % 0.16 s 4 cores @ 2.5 Ghz (Python)
121 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.02 % 0.05 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
122 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 EM-FPS 79.24 % 84.28 % 71.22 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
2 RRC code 76.49 % 84.96 % 65.46 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
3 VCTNet 75.91 % 83.20 % 67.81 % 0.02 s GPU @ 1.5 Ghz (C/C++)
4 SAITv1 75.83 % 83.99 % 66.45 % 0.15 s GPU @ 2.5 Ghz (C/C++)
5 CLA 74.68 % 82.42 % 65.11 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
6 MS-CNN code 74.45 % 82.34 % 64.91 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
7 TuSimple code 74.26 % 81.38 % 64.88 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
8 Deep3DBox 73.48 % 82.65 % 64.11 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
9 SDP+RPN 73.08 % 81.05 % 64.88 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
10 ECV-NET 72.73 % 82.62 % 62.82 % 0.4 s GPU @ 2.5 Ghz (C/C++)
11 sensekitti code 72.50 % 81.76 % 64.00 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
12 F-PointNet
This method makes use of Velodyne laser scans.
code 72.25 % 84.90 % 65.14 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
13 BOE_IOT_AIBD 71.61 % 82.63 % 63.67 % 0.8 s GPU @ 2.5 Ghz (Python)
14 SubCNN 70.77 % 77.82 % 62.71 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
15 Sogo_MM 70.72 % 77.57 % 62.23 % 1.5 s GPU @ 2.5 Ghz (C/C++)
16 ARPNET 69.94 % 79.83 % 63.08 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
17 ODES code 69.80 % 78.51 % 61.32 % 0.02 s GPU @ 2.5 Ghz (Python)
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18 MonoPSR 68.99 % 79.80 % 60.19 % 0.2 s GPU @ 3.5 Ghz (Python)
19 MDC
This method makes use of Velodyne laser scans.
68.84 % 79.81 % 60.24 % 0.2 s volta v100
20 3DOP
This method uses stereo information.
code 68.81 % 80.17 % 61.36 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
21 PointPillars
This method makes use of Velodyne laser scans.
68.57 % 82.59 % 62.37 % 16 ms 1080ti GPU and Intel i7 CPU
22 Pose-RCNN 68.04 % 80.19 % 59.95 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
23 Vote3Deep
This method makes use of Velodyne laser scans.
67.96 % 76.49 % 62.88 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
24 IVA code 67.36 % 77.63 % 59.62 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
25 DeepStereoOP 65.72 % 77.00 % 57.74 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
26 IPOD 65.28 % 82.90 % 57.63 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. arXiv preprint arXiv:1812.05276 2018.
27 retinanetkitti 64.44 % 77.60 % 57.66 % 1.5 s 1 core @ 2.5 Ghz (Python)
28 Mono3D code 63.85 % 75.22 % 58.96 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
29 CONV-BOX
This method makes use of Velodyne laser scans.
63.84 % 72.62 % 56.69 % 0.2 s Tesla V100
30 DA 63.58 % 79.36 % 56.80 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
31 Faster R-CNN code 62.81 % 71.41 % 55.44 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
32 SDP+CRC (ft) 60.87 % 74.31 % 53.95 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
33 merge12-12 60.83 % 75.12 % 53.69 % 0.2 s 4 cores @ 2.5 Ghz (Python)
34 ELLIOT
This method makes use of Velodyne laser scans.
60.04 % 77.40 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 AVOD-FPN
This method makes use of Velodyne laser scans.
code 59.32 % 68.65 % 55.82 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
36 SECOND code 58.94 % 81.96 % 57.20 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
37 Regionlets 58.69 % 70.09 % 51.81 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
38 cascadercnn 58.09 % 75.56 % 50.19 % 0.36 s 4 cores @ 2.5 Ghz (Python)
39 Complexer-YOLO
This method makes use of Velodyne laser scans.
57.53 % 65.82 % 57.47 % 0.06 s GPU @ 3.5 Ghz (C/C++)
40 FRCNN+Or code 57.37 % 70.05 % 51.00 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
41 bin 57.13 % 63.05 % 50.64 % 15ms s GPU @ >3.5 Ghz (Python)
42 AVOD
This method makes use of Velodyne laser scans.
code 56.01 % 65.72 % 48.89 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
43 ReSqueeze 54.93 % 68.34 % 49.19 % 0.03 s GPU @ >3.5 Ghz (Python)
44 NLK 52.74 % 60.66 % 48.49 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
45 anm 50.54 % 67.40 % 45.22 % 3 s 1 core @ 2.5 Ghz (C/C++)
46 tiny-det 50.48 % 63.78 % 44.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
47 ZKNet 50.24 % 66.44 % 44.19 % 0.01 s GPU @ 2.0 Ghz (Python)
48 LPN 50.02 % 65.33 % 44.85 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
49 yolo800 49.15 % 64.64 % 43.58 % 0.13 s 4 cores @ 2.5 Ghz (Python)
50 BirdNet
This method makes use of Velodyne laser scans.
49.04 % 64.88 % 46.61 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
51 fasterrcnn 48.81 % 64.40 % 42.74 % 0.2 s 4 cores @ 2.5 Ghz (Python)
52 X_MD 48.07 % 63.46 % 40.76 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
53 detectron code 48.06 % 64.73 % 40.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
54 RFCN 47.61 % 62.17 % 43.74 % 0.2 s 4 cores @ 2.5 Ghz (Python)
55 CLF3D
This method makes use of Velodyne laser scans.
47.53 % 65.31 % 40.23 % 0.13 s GPU @ 2.5 Ghz (Python)
56 RFCN_RFB 45.36 % 59.49 % 41.63 % 0.2 s 4 cores @ 2.5 Ghz (Python)
57 FD2 44.29 % 62.32 % 40.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
58 SeRC 44.28 % 55.81 % 38.50 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
59 Cmerge 43.85 % 61.60 % 42.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
60 MTDP 43.08 % 54.53 % 38.79 % 0.15 s GPU @ 2.0 Ghz (Python)
61 GNN 42.65 % 59.43 % 37.72 % 0.2 s 1 core @ 2.5 Ghz (Python)
62 MV-RGBD-RF
This method makes use of Velodyne laser scans.
42.61 % 51.46 % 37.42 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
63 YOLOv3+d 42.60 % 59.08 % 40.77 % 0.04 s GPU @ 1.5 Ghz (C/C++)
64 SS3D 40.96 % 56.34 % 35.17 % 88 ms Tesla V100 (Python)
65 pAUCEnsT 37.88 % 52.28 % 33.38 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
66 Retinanet100 37.54 % 46.39 % 30.82 % 0.2 s 4 cores @ 2.5 Ghz (Python)
67 softyolo 31.30 % 45.16 % 27.38 % 0.16 s 4 cores @ 2.5 Ghz (Python)
68 Vote3D
This method makes use of Velodyne laser scans.
31.24 % 41.45 % 28.60 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
69 DPM-VOC+VP 31.16 % 43.65 % 28.29 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
70 LSVM-MDPM-us code 30.81 % 40.31 % 28.17 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
71 100Frcnn 29.95 % 44.60 % 27.70 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
72 LSVM-MDPM-sv 29.24 % 37.71 % 27.52 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
73 DPM-C8B1
This method uses stereo information.
29.04 % 43.28 % 26.20 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
74 R-CNN_VGG 28.79 % 37.71 % 25.82 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
75 rpn 28.65 % 37.40 % 23.50 % 0.01 s 1 core @ 2.5 Ghz (Python)
76 Lidar_ROI+Yolo(UJS) 27.21 % 39.41 % 26.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 mBoW
This method makes use of Velodyne laser scans.
21.62 % 28.19 % 20.93 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
78 DT3D 20.65 % 31.29 % 20.73 % 0,21s GPU @ 2.5 Ghz (Python)
79 TopNet-HighRes
This method makes use of Velodyne laser scans.
19.15 % 29.34 % 19.69 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
80 KD53-20 17.71 % 23.15 % 17.30 % 0.19 s 4 cores @ 2.5 Ghz (Python)
81 SA-3D
This method makes use of Velodyne laser scans.
15.82 % 20.11 % 12.83 % 0.3 s 1 core @ 2.5 Ghz (Python)
82 Kyolo3 9.09 % 9.09 % 9.09 % 0.16 s 4 cores @ 2.5 Ghz (Python)
83 Fast-SSD 7.10 % 11.77 % 7.23 % 0.06 s GTX650Ti
84 KD45 5.87 % 5.96 % 4.53 % 0.16 s 4 cores @ 2.5 Ghz (Python)
85 YOLOv2 code 4.55 % 4.55 % 4.55 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
86 TopNet-DecayRate
This method makes use of Velodyne laser scans.
1.01 % 0.04 % 1.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
87 softretina 0.44 % 0.29 % 0.22 % 0.16 s 4 cores @ 2.5 Ghz (Python)
88 JSyolo 0.22 % 0.22 % 0.22 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 MVRA + I-FRCNN+ 89.93 % 90.60 % 79.78 % 0.18 s GPU @ 2.5 Ghz (Python)
2 Deep MANTA 89.86 % 97.19 % 80.39 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
3 PointRCNN
This method makes use of Velodyne laser scans.
89.55 % 90.76 % 80.76 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
4 PointPillars
This method makes use of Velodyne laser scans.
88.76 % 90.19 % 86.38 % 16 ms 1080ti GPU and Intel i7 CPU
5 Sogo_MM 88.72 % 90.67 % 78.95 % 1.5 s GPU @ 2.5 Ghz (C/C++)
6 Deep3DBox 88.56 % 90.39 % 77.17 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
7 SubCNN 88.43 % 90.61 % 78.63 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
8 LTT
This method makes use of Velodyne laser scans.
87.90 % 89.83 % 80.72 % 0.4 s 1 core @ 3.5 Ghz (Python)
9 MonoPSR 87.83 % 89.88 % 70.48 % 0.2 s GPU @ 3.5 Ghz (Python)
10 ARPNET 87.68 % 89.79 % 79.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
11 AVOD
This method makes use of Velodyne laser scans.
code 87.46 % 89.59 % 79.54 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
12 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
87.34 % 89.88 % 79.32 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
13 AVOD-FPN
This method makes use of Velodyne laser scans.
code 87.13 % 89.95 % 79.74 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
14 DFD 87.01 % 89.72 % 78.98 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
15 SECA 86.80 % 89.42 % 78.81 % 0.09 s GPU @ 2.5 Ghz (Python)
16 SCANet 86.65 % 89.06 % 78.67 % 0.09s GPU @ 2.5 Ghz (Python)
17 DeepStereoOP 86.57 % 89.01 % 77.13 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
18 ELLIOT
This method makes use of Velodyne laser scans.
86.13 % 89.69 % 80.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 Mono3D code 85.83 % 89.00 % 76.00 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
20 3DOP
This method uses stereo information.
code 85.81 % 88.56 % 76.21 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
21 MBR-SSD 85.03 % 88.10 % 75.92 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
22 SECOND code 81.31 % 87.84 % 71.95 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
23 Stereo R-CNN
This method uses stereo information.
80.51 % 90.30 % 71.04 % 0.4 s GPU @ 2.5 Ghz (Python)
24 X_MD 80.28 % 89.53 % 79.14 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
25 FNV1_Fusion 80.12 % 89.25 % 78.58 % 0.11 s GPU @ 2.5 Ghz (Python)
26 FNV1_RPN 80.10 % 89.27 % 78.66 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
27 SS3D 79.77 % 89.18 % 69.99 % 88 ms Tesla V100 (Python)
28 VSE 79.56 % 89.11 % 78.14 % 0.15 s GPU @ 2.5 Ghz (Python)
29 Complexer-YOLO
This method makes use of Velodyne laser scans.
79.08 % 87.97 % 78.75 % 0.06 s GPU @ 3.5 Ghz (C/C++)
30 FNV1 78.97 % 88.40 % 76.70 % 0.11 s GPU @ 2.5 Ghz (Python)
31 FRCNN+Or code 77.61 % 88.52 % 67.69 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
32 CLF3D
This method makes use of Velodyne laser scans.
76.50 % 84.35 % 67.12 % 0.13 s GPU @ 2.5 Ghz (Python)
33 avodC 76.30 % 86.31 % 68.71 % 0.1 s GPU @ 2.5 Ghz (Python)
34 3D FCN
This method makes use of Velodyne laser scans.
75.71 % 85.46 % 68.19 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
35 3D-SSMFCNN code 75.42 % 75.44 % 67.27 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
36 Pose-RCNN 75.35 % 88.78 % 61.47 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
37 GS3D 75.16 % 83.52 % 59.59 % 0.58 s GPU @ 2.5 Ghz (C/C++)
38 3DVP code 74.59 % 81.02 % 64.11 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
39 SubCat code 74.42 % 80.74 % 58.83 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
40 BdCost+ 73.15 % 82.12 % 58.29 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
41 ROI-10D 67.85 % 74.24 % 59.28 % 0.2 s GPU @ 3.5 Ghz (Python)
42 MF3D 67.68 % 87.79 % 57.57 % 0.03 s GPU @ 2.5 Ghz (C/C++)
43 multi-task CNN 66.19 % 76.69 % 58.11 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
44 BdCost48LDCF code 66.01 % 77.10 % 50.35 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
45 BdCost48-25C 65.25 % 77.59 % 50.68 % 4 s 1 core @ 2.5 Ghz (C/C++)
46 OC-DPM 64.88 % 74.66 % 52.24 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
47 3DVSSD 64.72 % 77.22 % 57.56 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
48 DPM-VOC+VP 63.27 % 77.51 % 47.57 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
49 AOG-View 62.25 % 77.37 % 50.44 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
50 SAMME48LDCF code 57.49 % 75.12 % 46.64 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
51 LSVM-MDPM-sv 56.69 % 70.86 % 45.91 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
52 RCN-resnet101 53.93 % 56.36 % 48.32 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
53 SAG-Net 53.29 % 57.92 % 47.73 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
54 VeloFCN
This method makes use of Velodyne laser scans.
52.70 % 70.21 % 46.11 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
55 desNet 51.78 % 54.13 % 46.46 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
56 vfnet 50.96 % 53.53 % 45.91 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
57 DPM-C8B1
This method uses stereo information.
50.32 % 59.53 % 39.22 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
58 VAT-Net 49.91 % 52.74 % 45.16 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
59 InNet 49.55 % 52.32 % 44.79 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
60 CNN-ds code 48.12 % 46.39 % 38.40 % 0.05 s 1 core @ 2.5 Ghz (Python)
61 ODES code 48.06 % 46.22 % 42.43 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
62 ReSqueeze 45.40 % 47.38 % 41.68 % 0.03 s GPU @ >3.5 Ghz (Python)
63 sensekitti code 44.56 % 47.06 % 41.50 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
64 VCTNet 43.41 % 46.53 % 39.42 % 0.02 s GPU @ 1.5 Ghz (C/C++)
65 Resnet101Faster rcnn 42.62 % 49.41 % 38.21 % 1 s 1 core @ 2.5 Ghz (Python)
66 FD2 39.44 % 47.56 % 35.20 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
67 bin 37.23 % 41.94 % 32.65 % 15ms s GPU @ >3.5 Ghz (Python)
68 IPOD 37.01 % 36.95 % 36.96 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. arXiv preprint arXiv:1812.05276 2018.
69 merge12-12 36.02 % 36.92 % 31.88 % 0.2 s 4 cores @ 2.5 Ghz (Python)
70 BirdNet
This method makes use of Velodyne laser scans.
35.81 % 50.85 % 34.90 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
71 cascadercnn 35.01 % 34.13 % 28.55 % 0.36 s 4 cores @ 2.5 Ghz (Python)
72 Cmerge 32.95 % 36.87 % 29.14 % 0.2 s 4 cores @ 2.5 Ghz (Python)
73 ZKNet 32.91 % 37.16 % 28.94 % 0.01 s GPU @ 2.0 Ghz (Python)
74 softretina 32.90 % 37.63 % 28.73 % 0.16 s 4 cores @ 2.5 Ghz (Python)
75 Fast-SSD 32.90 % 40.88 % 29.21 % 0.06 s GTX650Ti
76 Retinanet100 32.87 % 37.54 % 28.69 % 0.2 s 4 cores @ 2.5 Ghz (Python)
77 LPN 32.41 % 33.97 % 29.15 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
78 SceneNet 32.02 % 36.62 % 28.46 % 0.03 s GPU @ 2.5 Ghz (C/C++)
79 detectron code 31.71 % 35.58 % 28.18 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
80 RFCN_RFB 31.47 % 33.70 % 27.02 % 0.2 s 4 cores @ 2.5 Ghz (Python)
81 MTDP 31.04 % 34.12 % 27.50 % 0.15 s GPU @ 2.0 Ghz (Python)
82 AOG code 30.81 % 34.05 % 24.86 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
83 yolo800 30.47 % 31.53 % 27.01 % 0.13 s 4 cores @ 2.5 Ghz (Python)
84 RFCN 30.29 % 33.30 % 25.44 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 Fast-SSD 29.60 % 40.48 % 25.85 % 0.06 s GTX650Ti
86 fasterrcnn 28.13 % 29.83 % 24.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
87 VoxelNet(Unofficial) 27.26 % 26.86 % 25.12 % 0.5 s GPU @ 2.0 Ghz (Python)
88 SubCat48LDCF code 26.78 % 34.43 % 19.46 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
89 RFBnet 26.39 % 32.45 % 23.97 % 0.2 s 4 cores @ 2.5 Ghz (Python)
90 Lidar_ROI+Yolo(UJS) 25.40 % 28.93 % 22.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
91 CSoR
This method makes use of Velodyne laser scans.
code 25.38 % 34.43 % 21.95 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
92 100Frcnn 25.26 % 34.82 % 21.73 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
93 RT3D
This method makes use of Velodyne laser scans.
18.98 % 24.23 % 20.56 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
94 softyolo 18.22 % 25.50 % 15.97 % 0.16 s 4 cores @ 2.5 Ghz (Python)
95 Kyolo3 18.21 % 19.50 % 15.99 % 0.16 s 4 cores @ 2.5 Ghz (Python)
96 VoxelNet basic
This method makes use of Velodyne laser scans.
18.12 % 18.64 % 16.65 % 0.07 s GPU (Python)
97 rpn 17.04 % 25.68 % 13.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
98 KD45 16.04 % 16.89 % 14.96 % 0.16 s 4 cores @ 2.5 Ghz (Python)
99 Licar
This method makes use of Velodyne laser scans.
15.58 % 18.24 % 16.15 % 0.09 s GPU @ 2.0 Ghz (Python)
100 KD53-20 14.27 % 20.79 % 12.61 % 0.19 s 4 cores @ 2.5 Ghz (Python)
101 DLnet 8.48 % 9.09 % 7.39 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
102 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
103 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Pedestrians


Method Setting Code Moderate Easy Hard Runtime Environment
1 Sogo_MM 66.83 % 78.89 % 62.06 % 1.5 s GPU @ 2.5 Ghz (C/C++)
2 SubCNN 66.28 % 78.33 % 61.37 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
3 Pose-RCNN 59.89 % 74.10 % 54.21 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
4 3DOP
This method uses stereo information.
code 59.79 % 73.46 % 57.04 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
5 DeepStereoOP 59.28 % 73.37 % 56.87 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
6 Mono3D code 58.12 % 68.58 % 54.94 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
7 MonoPSR 56.30 % 70.56 % 49.84 % 0.2 s GPU @ 3.5 Ghz (Python)
8 FRCNN+Or code 52.62 % 66.84 % 48.72 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
9 PointPillars
This method makes use of Velodyne laser scans.
49.66 % 58.05 % 47.88 % 16 ms 1080ti GPU and Intel i7 CPU
10 CLF3D
This method makes use of Velodyne laser scans.
46.86 % 62.19 % 44.92 % 0.13 s GPU @ 2.5 Ghz (Python)
11 AVOD-FPN
This method makes use of Velodyne laser scans.
code 44.92 % 53.36 % 43.77 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
12 SECOND code 43.51 % 51.56 % 38.78 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
13 ARPNET 42.40 % 54.90 % 41.21 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
14 DPM-VOC+VP 39.83 % 53.66 % 35.73 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
15 SS3D 39.04 % 53.75 % 37.76 % 88 ms Tesla V100 (Python)
16 VCTNet 38.60 % 43.57 % 36.82 % 0.02 s GPU @ 1.5 Ghz (C/C++)
17 HBA-RCNN 38.06 % 43.81 % 35.02 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
18 sensekitti code 37.50 % 43.55 % 35.08 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
19 AVOD
This method makes use of Velodyne laser scans.
code 36.38 % 44.12 % 31.81 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
20 LSVM-MDPM-sv 35.49 % 47.00 % 32.42 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
21 IPOD 35.32 % 41.46 % 31.59 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. arXiv preprint arXiv:1812.05276 2018.
22 SubCat 34.18 % 43.95 % 30.76 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
23 CHTTL MMF 34.17 % 43.98 % 30.89 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
24 ELLIOT
This method makes use of Velodyne laser scans.
34.11 % 41.90 % 32.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 cascadercnn 33.27 % 43.05 % 28.88 % 0.36 s 4 cores @ 2.5 Ghz (Python)
26 merge12-12 32.94 % 42.47 % 31.87 % 0.2 s 4 cores @ 2.5 Ghz (Python)
27 RPN+BF code 32.55 % 40.97 % 29.52 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
28 RFCN 32.48 % 40.51 % 28.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
29 X_MD 32.45 % 43.55 % 31.29 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
30 ReSqueeze 32.35 % 37.95 % 30.38 % 0.03 s GPU @ >3.5 Ghz (Python)
31 bin 31.81 % 36.25 % 29.83 % 15ms s GPU @ >3.5 Ghz (Python)
32 Complexer-YOLO
This method makes use of Velodyne laser scans.
31.80 % 37.80 % 31.26 % 0.06 s GPU @ 3.5 Ghz (C/C++)
33 LPN 31.63 % 38.40 % 28.90 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
34 ZKNet 31.58 % 39.11 % 28.78 % 0.01 s GPU @ 2.0 Ghz (Python)
35 ODES code 31.43 % 36.84 % 29.00 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
36 yolo800 31.42 % 40.42 % 30.50 % 0.13 s 4 cores @ 2.5 Ghz (Python)
37 detectron code 31.20 % 41.08 % 30.78 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
38 RFCN_RFB 30.38 % 38.00 % 26.72 % 0.2 s 4 cores @ 2.5 Ghz (Python)
39 fasterrcnn 29.82 % 38.69 % 28.45 % 0.2 s 4 cores @ 2.5 Ghz (Python)
40 MTDP 29.04 % 36.90 % 25.96 % 0.15 s GPU @ 2.0 Ghz (Python)
41 FD2 28.59 % 35.53 % 26.02 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
42 ACF 28.46 % 35.69 % 26.18 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
43 multi-task CNN 26.98 % 33.58 % 23.07 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
44 softyolo 26.04 % 34.86 % 25.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
45 Cmerge 24.19 % 34.08 % 24.04 % 0.2 s 4 cores @ 2.5 Ghz (Python)
46 Resnet101Faster rcnn 23.89 % 30.25 % 23.38 % 1 s 1 core @ 2.5 Ghz (Python)
47 Lidar_ROI+Yolo(UJS) 23.43 % 28.50 % 19.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 DPM-C8B1
This method uses stereo information.
23.37 % 31.08 % 20.72 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
49 Retinanet100 23.23 % 28.72 % 19.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
50 ACF-MR 23.18 % 29.35 % 21.00 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
51 KD53-20 22.24 % 26.50 % 19.80 % 0.19 s 4 cores @ 2.5 Ghz (Python)
52 rpn 22.07 % 30.16 % 21.44 % 0.01 s 1 core @ 2.5 Ghz (Python)
53 KD45 21.35 % 30.55 % 19.36 % 0.16 s 4 cores @ 2.5 Ghz (Python)
54 100Frcnn 18.55 % 23.61 % 18.34 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
55 BirdNet
This method makes use of Velodyne laser scans.
17.26 % 21.34 % 16.67 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
56 Kyolo3 9.67 % 12.06 % 9.27 % 0.16 s 4 cores @ 2.5 Ghz (Python)
57 Fast-SSD 9.16 % 12.68 % 9.01 % 0.06 s GTX650Ti
58 softretina 0.49 % 0.35 % 0.50 % 0.16 s 4 cores @ 2.5 Ghz (Python)
59 JSyolo 0.23 % 0.20 % 0.25 % 0.16 s 4 cores @ 2.5 Ghz (Python)
60 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods


Cyclists


Method Setting Code Moderate Easy Hard Runtime Environment
1 ARPNET 69.13 % 79.33 % 62.24 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
2 PointPillars
This method makes use of Velodyne laser scans.
68.16 % 82.43 % 61.96 % 16 ms 1080ti GPU and Intel i7 CPU
3 Sogo_MM 63.59 % 70.70 % 56.15 % 1.5 s GPU @ 2.5 Ghz (C/C++)
4 SubCNN 63.41 % 71.39 % 56.34 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
5 Pose-RCNN 62.25 % 74.85 % 55.09 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
6 Deep3DBox 59.37 % 68.58 % 51.97 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
7 3DOP
This method uses stereo information.
code 58.59 % 71.95 % 52.35 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
8 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.53 % 67.61 % 54.16 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
9 SECOND code 57.20 % 80.97 % 55.14 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
10 ELLIOT
This method makes use of Velodyne laser scans.
56.42 % 74.07 % 51.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 Complexer-YOLO
This method makes use of Velodyne laser scans.
56.32 % 64.51 % 56.23 % 0.06 s GPU @ 3.5 Ghz (C/C++)
12 DeepStereoOP 55.62 % 67.49 % 48.85 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
13 AVOD
This method makes use of Velodyne laser scans.
code 54.43 % 64.36 % 47.67 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
14 Mono3D code 53.11 % 65.74 % 48.87 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
15 FRCNN+Or code 50.91 % 63.41 % 45.46 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
16 MonoPSR 49.30 % 58.93 % 43.45 % 0.2 s GPU @ 3.5 Ghz (Python)
17 CLF3D
This method makes use of Velodyne laser scans.
46.66 % 64.55 % 39.30 % 0.13 s GPU @ 2.5 Ghz (Python)
18 X_MD 45.90 % 61.86 % 39.14 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
19 sensekitti code 42.12 % 46.65 % 36.66 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
20 VCTNet 37.64 % 46.21 % 33.46 % 0.02 s GPU @ 1.5 Ghz (C/C++)
21 ODES code 33.74 % 37.75 % 30.34 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
22 SS3D 32.61 % 45.90 % 28.30 % 88 ms Tesla V100 (Python)
23 BirdNet
This method makes use of Velodyne laser scans.
30.76 % 41.48 % 28.66 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
24 bin 29.53 % 34.66 % 25.98 % 15ms s GPU @ >3.5 Ghz (Python)
25 IPOD 28.88 % 36.04 % 25.66 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. arXiv preprint arXiv:1812.05276 2018.
26 ReSqueeze 27.40 % 35.39 % 24.32 % 0.03 s GPU @ >3.5 Ghz (Python)
27 LPN 27.01 % 32.96 % 25.01 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
28 merge12-12 26.91 % 32.25 % 23.72 % 0.2 s 4 cores @ 2.5 Ghz (Python)
29 cascadercnn 26.62 % 33.02 % 23.01 % 0.36 s 4 cores @ 2.5 Ghz (Python)
30 detectron code 26.36 % 27.44 % 23.20 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
31 FD2 24.65 % 35.58 % 21.97 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
32 DPM-VOC+VP 23.22 % 31.24 % 21.62 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
33 LSVM-MDPM-sv 23.14 % 28.89 % 22.28 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
34 fasterrcnn 22.08 % 28.60 % 19.31 % 0.2 s 4 cores @ 2.5 Ghz (Python)
35 yolo800 21.69 % 28.20 % 19.53 % 0.13 s 4 cores @ 2.5 Ghz (Python)
36 ZKNet 21.50 % 28.20 % 19.12 % 0.01 s GPU @ 2.0 Ghz (Python)
37 RFCN 20.80 % 26.55 % 19.22 % 0.2 s 4 cores @ 2.5 Ghz (Python)
38 RFCN_RFB 20.44 % 25.95 % 18.78 % 0.2 s 4 cores @ 2.5 Ghz (Python)
39 DPM-C8B1
This method uses stereo information.
19.25 % 27.16 % 17.95 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
40 Cmerge 19.19 % 26.37 % 18.56 % 0.2 s 4 cores @ 2.5 Ghz (Python)
41 MTDP 18.95 % 23.33 % 17.24 % 0.15 s GPU @ 2.0 Ghz (Python)
42 Retinanet100 15.16 % 18.64 % 12.49 % 0.2 s 4 cores @ 2.5 Ghz (Python)
43 softyolo 12.14 % 16.84 % 10.51 % 0.16 s 4 cores @ 2.5 Ghz (Python)
44 100Frcnn 11.79 % 17.33 % 10.99 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
45 rpn 11.30 % 14.62 % 8.94 % 0.01 s 1 core @ 2.5 Ghz (Python)
46 Lidar_ROI+Yolo(UJS) 9.31 % 13.88 % 9.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 KD53-20 6.15 % 7.81 % 6.35 % 0.19 s 4 cores @ 2.5 Ghz (Python)
48 Fast-SSD 4.55 % 6.94 % 4.55 % 0.06 s GTX650Ti
49 Kyolo3 3.96 % 3.96 % 3.96 % 0.16 s 4 cores @ 2.5 Ghz (Python)
50 KD45 2.23 % 2.33 % 1.68 % 0.16 s 4 cores @ 2.5 Ghz (Python)
51 softretina 0.20 % 0.14 % 0.10 % 0.16 s 4 cores @ 2.5 Ghz (Python)
52 JSyolo 0.15 % 0.15 % 0.15 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Related Datasets

Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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