\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
RRC & & 76.47 \% & 84.96 \% & 65.46 \% & 3.6 s / GPU & 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.\\
SAIT & & 75.88 \% & 83.99 \% & 66.45 \% & 0.15 s / GPU & \\
Pie & & 75.86 \% & 81.70 \% & 66.99 \% & 1.2 s / 1 core & \\
TiCNN & & 74.99 \% & 81.90 \% & 65.40 \% & 0.5 s / GPU & \\
MS-CNN & & 74.45 \% & 82.34 \% & 64.91 \% & 0.4 s / GPU & Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.\\
TuSimple & & 74.26 \% & 81.38 \% & 64.88 \% & 1.6 s / GPU & \\
Allspark & & 74.25 \% & 81.77 \% & 65.23 \% & 0.7 s / GPU & \\
Deep3DBox & & 73.48 \% & 82.65 \% & 64.11 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
SDP+RPN & & 73.08 \% & 81.05 \% & 64.88 \% & 0.4 s / GPU & 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.\\
sensekitti & & 72.50 \% & 81.76 \% & 64.00 \% & 4.5 s / GPU & \\
SubCNN & & 70.77 \% & 77.82 \% & 62.71 \% & 2 s / GPU & 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.\\
uickitti & & 70.72 \% & 77.57 \% & 62.23 \% & 1.5 s / GPU & \\
DJML & & 70.32 \% & 78.76 \% & 61.89 \% & 2.4 s / GPU & \\
3DOP & st & 68.81 \% & 80.17 \% & 61.36 \% & 3s / GPU & 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.\\
Re-3DOP & & 68.44 \% & 78.46 \% & 60.80 \% & 3 s / 1 core & \\
Pose-RCNN & & 68.04 \% & 80.19 \% & 59.95 \% & 2 s / >8 cores & \\
Vote3Deep & la & 67.96 \% & 76.49 \% & 62.88 \% & 1.5 s / 4 cores & 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.\\
IVA & & 67.36 \% & 77.63 \% & 59.62 \% & 0.4 s / GPU & 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.\\
DeepStereoOP & & 65.72 \% & 77.00 \% & 57.74 \% & 3.4 s / GPU & 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.\\
HSR2 & & 64.94 \% & 76.36 \% & 57.62 \% & 0.15 s / 1 core & \\
HM\_SSD\_RCNN & & 64.67 \% & 77.55 \% & 54.70 \% & 0.15 s / 1 core & \\
Mono3D & & 63.85 \% & 75.22 \% & 58.96 \% & 4.2 s / GPU & X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.\\
tbd & & 63.48 \% & 75.49 \% & 55.88 \% & 1 s / 1 core & \\
WRInception & & 62.85 \% & 78.19 \% & 55.64 \% & 0.06 s / GPU & \\
Faster R-CNN & & 62.81 \% & 71.41 \% & 55.44 \% & 2 s / GPU & S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.\\
IVA & & 60.99 \% & 67.88 \% & 54.34 \% & 1 s / GPU & \\
SDP+CRC (ft) & & 60.87 \% & 74.31 \% & 53.95 \% & 0.6 s / GPU & 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.\\
PNET & & 58.70 \% & 73.97 \% & 51.63 \% & 0.1 s / GPU & \\
Regionlets & & 58.69 \% & 70.09 \% & 51.81 \% & 1 s / >8 cores & 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.\\
ANM & & 53.04 \% & 71.56 \% & 46.38 \% & 0.05 s / GPU & \\
ANM & & 52.95 \% & 69.91 \% & 46.80 \% & 0.05 s / GPU & \\
ZGC & & 48.06 \% & 64.87 \% & 40.74 \% & 0.12 s / 1 core & \\
FD2 & & 44.29 \% & 62.32 \% & 40.65 \% & 0.01 s / GPU & \\
maxFtr+ROI & & 43.59 \% & 49.65 \% & 38.74 \% & 0.25 s / 4 cores & W. Tian and M. Lauer: Detection and Orientation Estimation for Cyclists by Max Pooled Features. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2017.\\
MV-RGBD-RF & la & 42.61 \% & 51.46 \% & 37.42 \% & 4 s / 4 cores & 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.\\
QHY & & 42.30 \% & 59.30 \% & 41.29 \% & 0.1 s / 1 core & \\
NMF-CNN & & 42.13 \% & 56.30 \% & 37.46 \% & 0.1 s / GPU & \\
AR-FCN & & 41.83 \% & 51.05 \% & 33.99 \% & 0.19 s / GPU & \\
RCNN & & 40.38 \% & 50.77 \% & 33.07 \% & 0.08 s / GPU & \\
HL & & 39.10 \% & 55.19 \% & 32.66 \% & 0.16 s / 1 core & \\
pAUCEnsT & & 37.88 \% & 52.28 \% & 33.38 \% & 60 s / 1 core & S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.\\
FD & & 37.01 \% & 51.41 \% & 32.93 \% & 0.01 s / GPU & \\
NMRDO & & 33.43 \% & 46.39 \% & 27.79 \% & 0.1 s / GPU & \\
Vote3D & la & 31.24 \% & 41.45 \% & 28.60 \% & 0.5 s / 4 cores & D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.\\
DPM-VOC+VP & & 31.16 \% & 43.65 \% & 28.29 \% & 8 s / 1 core & 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.\\
LSVM-MDPM-us & & 30.81 \% & 40.31 \% & 28.17 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
ACF\_M & & 29.24 \% & 37.71 \% & 27.52 \% & 0.1 s / 1 core & \\
LSVM-MDPM-sv & & 29.24 \% & 37.71 \% & 27.52 \% & 10 s / 4 cores & 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.\\
DPM-C8B1 & st & 29.04 \% & 43.28 \% & 26.20 \% & 15 s / 4 cores & 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.\\
R-CNN\_VGG & & 28.79 \% & 37.71 \% & 25.82 \% & 10 s / GPU & \\
mBoW & la & 21.62 \% & 28.19 \% & 20.93 \% & 10 s / 1 core & 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.\\
YOLO & & 13.96 \% & 18.07 \% & 13.83 \% & 0.03 s / GPU & \\
YOLOv2 & & 4.55 \% & 4.55 \% & 4.55 \% & 0.02 s / GPU & 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.
\end{tabular}