\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
SubCNN \cite{xiang2017subcategory} & 66.28 \% & 78.33 \% & 61.37 \% & 2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 59.79 \% & 73.46 \% & 57.04 \% & 3s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 59.28 \% & 73.37 \% & 56.87 \% & 3.4 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 58.12 \% & 68.58 \% & 54.94 \% & 4.2 s / GPU \\
FRCNN+Or \cite{GuindelICVES} & 52.96 \% & 67.92 \% & 49.61 \% & 0.1 s / GPU \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 39.83 \% & 53.66 \% & 35.73 \% & 8 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 35.49 \% & 47.00 \% & 32.42 \% & 10 s / 4 cores \\
SubCat \cite{OhnBar2014CVPRWORK} & 34.18 \% & 43.95 \% & 30.76 \% & 1.2 s / 6 cores \\
RPN+BF \cite{Zhang2016ECCV} & 32.55 \% & 40.97 \% & 29.52 \% & 0.6 s / GPU \\
ACF \cite{Dollar2014PAMI} & 28.46 \% & 35.69 \% & 26.18 \% & 1 s / 1 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 23.37 \% & 31.08 \% & 20.72 \% & 15 s / 4 cores \\
ACF-MR \cite{Nattoji2016TITS} & 23.18 \% & 29.35 \% & 21.00 \% & 0.6 s / 1 core
\end{tabular}