\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
RRC \cite{Ren17CVPR} & 75.33 \% & 84.14 \% & 70.39 \% & 3.6 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 73.62 \% & 83.70 \% & 68.28 \% & 0.4 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 71.34 \% & 83.17 \% & 66.36 \% & 2 s / GPU \\
IVA \cite{Zhu2016ACCV} & 70.63 \% & 83.03 \% & 64.68 \% & 0.4 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 70.20 \% & 79.98 \% & 64.84 \% & 0.4 s / GPU \\
3DOP \cite{Chen2015NIPS} & 67.46 \% & 82.36 \% & 64.71 \% & 3s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 67.32 \% & 82.50 \% & 65.14 \% & 3.4 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 66.66 \% & 77.30 \% & 63.44 \% & 4.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 65.91 \% & 78.35 \% & 61.19 \% & 2 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 64.25 \% & 77.81 \% & 59.31 \% & 0.6 s / GPU \\
CFM \cite{7807316} & 63.26 \% & 74.21 \% & 56.44 \% & \\
RPN+BF \cite{Zhang2016ECCV} & 61.29 \% & 75.58 \% & 56.08 \% & 0.6 s / GPU \\
Regionlets \cite{Wang2015PAMI} & 61.16 \% & 72.96 \% & 55.22 \% & 1 s / >8 cores \\
CompACT-Deep \cite{Cai2015ICCV} & 58.73 \% & 69.70 \% & 52.69 \% & 1 s / 1 core \\
DeepParts \cite{Tian2015ICCV} & 58.68 \% & 70.46 \% & 52.73 \% & ~1 s / GPU \\
FilteredICF \cite{Zhang2015CVPR} & 57.12 \% & 69.05 \% & 51.46 \% & ~ 2 s / >8 cores \\
FRCNN+Or \cite{GuindelICVES} & 56.99 \% & 72.21 \% & 53.72 \% & 0.1 s / GPU \\
D-TSF \cite{ERROR: Wrong syntax in BIBTEX file.} & 56.77 \% & 69.03 \% & 50.77 \% & 1 s / 1 core \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 56.59 \% & 73.05 \% & 49.63 \% & 4 s / 4 cores \\
Vote3Deep \cite{Engelcke2016ARXIV} & 55.38 \% & 67.94 \% & 52.62 \% & 1.5 s / 4 cores \\
pAUCEnsT \cite{Paul2014ARXIV} & 54.58 \% & 66.11 \% & 48.49 \% & 60 s / 1 core \\
PDV2 \cite{Shen2017PR} & 53.74 \% & 65.71 \% & 49.47 \% & 3.7 s / 1 core \\
R-CNN \cite{Hosang2015DnnForPedestrians} & 50.20 \% & 62.05 \% & 44.85 \% & 4 s / GPU \\
ACF \cite{Dollar2014PAMI} & 47.29 \% & 60.11 \% & 42.90 \% & 1 s / 1 core \\
Fusion-DPM \cite{Premebida2014IROS} & 46.67 \% & 59.38 \% & 42.05 \% & ~ 30 s / 1 core \\
ACF-MR \cite{Nattoji2016TITS} & 46.23 \% & 58.85 \% & 42.10 \% & 0.6 s / 1 core \\
HA-SSVM \cite{Xu2016IJCV} & 45.51 \% & 58.91 \% & 41.08 \% & 21 s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 44.86 \% & 59.60 \% & 40.37 \% & 8 s / 1 core \\
ACF-SC \cite{Cadena2015ICRA} & 44.77 \% & 54.20 \% & 39.57 \% & \\
SquaresICF \cite{Benenson2013Cvpr} & 44.42 \% & 57.47 \% & 40.08 \% & 1 s / GPU \\
SubCat \cite{OhnBar2014CVPRWORK} & 42.34 \% & 54.06 \% & 37.95 \% & 1.2 s / 6 cores \\
ACF \cite{Dollar2014PAMI} & 40.62 \% & 49.08 \% & 36.66 \% & 0.2 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 39.36 \% & 51.75 \% & 35.95 \% & 10 s / 4 cores \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 38.35 \% & 50.01 \% & 34.78 \% & 10 s / 4 cores \\
Vote3D \cite{Wang2015RSS} & 35.74 \% & 44.47 \% & 33.72 \% & 0.5 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 31.37 \% & 44.36 \% & 30.62 \% & 10 s / 1 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 29.03 \% & 38.96 \% & 25.61 \% & 15 s / 4 cores \\
YOLOv2 \cite{redmon2016you} & 16.19 \% & 20.80 \% & 15.43 \% & 0.02 s / GPU \\
BIP-HETERO \cite{Mekonnen2014ICPR} & 13.38 \% & 14.85 \% & 13.25 \% & ~2 s / 1 core
\end{tabular}