\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
uickitti & & 66.83 \% & 78.89 \% & 62.06 \% & 1.5 s / GPU & \\
SubCNN & & 66.28 \% & 78.33 \% & 61.37 \% & 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.\\
Pose-RCNN & & 59.89 \% & 74.10 \% & 54.21 \% & 2 s / >8 cores & \\
3DOP & st & 59.79 \% & 73.46 \% & 57.04 \% & 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.\\
DeepStereoOP & & 59.28 \% & 73.37 \% & 56.87 \% & 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.\\
DJML & & 58.13 \% & 69.87 \% & 52.62 \% & 2.4 s / GPU & \\
Mono3D & & 58.12 \% & 68.58 \% & 54.94 \% & 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.\\
DPM-VOC+VP & & 39.83 \% & 53.66 \% & 35.73 \% & 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.\\
Allspark & & 38.98 \% & 43.54 \% & 36.22 \% & 0.7 s / GPU & \\
sensekitti & & 37.50 \% & 43.55 \% & 35.08 \% & 4.5 s / GPU & \\
Re-3DOP & & 36.27 \% & 44.80 \% & 34.34 \% & 3 s / 1 core & \\
ACF\_M & & 35.49 \% & 47.00 \% & 32.42 \% & 0.1 s / 1 core & \\
LSVM-MDPM-sv & & 35.49 \% & 47.00 \% & 32.42 \% & 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.\\
WRInception & & 35.14 \% & 40.34 \% & 32.50 \% & 0.06 s / GPU & \\
HM\_SSD\_RCNN & & 34.38 \% & 42.11 \% & 30.73 \% & 0.15 s / 1 core & \\
SubCat & & 34.18 \% & 43.95 \% & 30.76 \% & 1.2 s / 6 cores & E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.\\
HSR2 & & 33.86 \% & 39.97 \% & 32.48 \% & 0.15 s / 1 core & \\
Tx & & 33.84 \% & 39.58 \% & 30.96 \% & 2 s / GPU & \\
RB & & 33.70 \% & 43.29 \% & 30.29 \% & 0.6 s / GPU & \\
NMRDO & & 33.06 \% & 44.95 \% & 31.83 \% & 0.1 s / GPU & \\
SSD1 & & 32.73 \% & 41.73 \% & 30.69 \% & 0.255 s / GPU & \\
RPN+BF & & 32.55 \% & 40.97 \% & 29.52 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
NMF-CNN & & 30.94 \% & 40.14 \% & 28.58 \% & 0.1 s / GPU & \\
ANM & & 30.04 \% & 39.60 \% & 27.56 \% & 0.05 s / GPU & \\
FD2 & & 28.59 \% & 35.53 \% & 26.02 \% & 0.01 s / GPU & \\
ANM & & 28.47 \% & 38.07 \% & 27.69 \% & 0.05 s / GPU & \\
ACF & & 28.46 \% & 35.69 \% & 26.18 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
FD & & 27.90 \% & 33.68 \% & 25.17 \% & 0.01 s / GPU & \\
ZGC & & 26.42 \% & 34.65 \% & 22.57 \% & 0.12 s / 1 core & \\
HL & & 24.21 \% & 32.32 \% & 20.43 \% & 0.16 s / 1 core & \\
DPM-C8B1 & st & 23.37 \% & 31.08 \% & 20.72 \% & 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.\\
ACF-MR & & 23.18 \% & 29.35 \% & 21.00 \% & 0.6 s / 1 core & R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.\\
QHY & & 21.79 \% & 30.60 \% & 21.41 \% & 0.1 s / 1 core &
\end{tabular}