\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf MOTA} & {\bf MOTP} & {\bf MT} & {\bf ML} & {\bf IDS} & {\bf FRAG} & {\bf Runtime}\\ \hline
SRK\_ODESA \cite{ODESA2020} & 69.88 \% & 75.07 \% & 45.02 \% & 8.25 \% & 191 & 1070 & 0.5 s / \\
TuSimple \cite{choi2015near} & 58.15 \% & 71.93 \% & 30.58 \% & 24.05 \% & 138 & 818 & 0.6 s / 1 core \\
Quasi-Dense \cite{pang2020quasidense} & 56.81 \% & 73.99 \% & 31.27 \% & 18.90 \% & 254 & 1121 & 0.07s / \\
CenterTrack \cite{zhou2020tracking} & 55.34 \% & 74.02 \% & 34.71 \% & 19.93 \% & 95 & 751 & 0.045s / \\
3D-TLSR \cite{nguyen20203d} & 54.00 \% & 73.03 \% & 29.55 \% & 23.71 \% & 100 & 835 & / 1 core \\
CD \cite{ERROR: Wrong syntax in BIBTEX file.} & 53.31 \% & 73.67 \% & 28.87 \% & 23.71 \% & 416 & 1169 & 20 s / 1 core \\
CAT \cite{isprsannalsIV2W5532019} & 52.35 \% & 71.57 \% & 34.36 \% & 23.71 \% & 206 & 804 & / \\
Be-Track \cite{s19020391} & 51.29 \% & 72.71 \% & 20.96 \% & 31.27 \% & 118 & 848 & 0.02 s / GPU \\
MDP \cite{Xiang2015ICCV} & 47.22 \% & 70.36 \% & 24.05 \% & 27.84 \% & 87 & 825 & 0.9 s / 8 cores \\
NOMT* \cite{Choi2015ICCV} & 46.62 \% & 71.45 \% & 26.12 \% & 34.02 \% & 63 & 666 & 0.09 s / 16 cores \\
JRMOT \cite{Shenoi2020JRMOTAR} & 46.33 \% & 72.54 \% & 23.37 \% & 28.87 \% & 345 & 1111 & 0.07 s / 4 cores \\
MCMOT-CPD \cite{Lee2016ECCVWORK} & 45.94 \% & 72.44 \% & 20.62 \% & 34.36 \% & 143 & 764 & 0.01 s / 1 core \\
JCSTD \cite{Tian2019MOT} & 44.20 \% & 72.09 \% & 16.49 \% & 33.68 \% & 53 & 917 & 0.07 s / 1 core \\
SCEA* \cite{Yoon2016CVPR} & 43.91 \% & 71.86 \% & 16.15 \% & 43.30 \% & 56 & 641 & 0.06 s / 1 core \\
RMOT* \cite{Yoon2015WACV} & 43.77 \% & 71.02 \% & 19.59 \% & 41.24 \% & 153 & 748 & 0.02 s / 1 core \\
LP-SSVM* \cite{Wang2016IJCV} & 43.76 \% & 70.48 \% & 20.62 \% & 34.36 \% & 73 & 809 & 0.02 s / 1 core \\
CIWT* \cite{Osep17ICRA} & 43.37 \% & 71.44 \% & 13.75 \% & 34.71 \% & 112 & 901 & 0.28 s / 1 core \\
AB3DMOT \cite{Weng2019} & 39.63 \% & 64.87 \% & 16.84 \% & 41.58 \% & 170 & 940 & 0.0047s / 1 core \\
NOMT-HM* \cite{Choi2015ICCV} & 39.26 \% & 71.14 \% & 21.31 \% & 41.92 \% & 184 & 863 & 0.09 s / 8 cores \\
NOMT \cite{Choi2015ICCV} & 36.93 \% & 67.75 \% & 17.87 \% & 42.61 \% & 34 & 789 & 0.09 s / 16 core \\
RMOT \cite{Yoon2015WACV} & 34.54 \% & 68.06 \% & 14.43 \% & 47.42 \% & 81 & 685 & 0.01 s / 1 core \\
LP-SSVM \cite{Wang2016IJCV} & 33.33 \% & 67.38 \% & 12.37 \% & 45.02 \% & 72 & 818 & 0.05 s / 1 core \\
SCEA \cite{Yoon2016CVPR} & 33.13 \% & 68.45 \% & 9.62 \% & 46.74 \% & 16 & 717 & 0.05 s / 1 core \\
CEM \cite{Milan2014PAMI} & 27.54 \% & 68.48 \% & 8.93 \% & 51.89 \% & 96 & 608 & 0.09 s / 1 core \\
NOMT-HM \cite{Choi2015ICCV} & 27.49 \% & 67.99 \% & 15.12 \% & 50.52 \% & 73 & 732 & 0.09 s / 8 cores \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 16.46 \% & 62.69 \% & 2.41 \% & 38.14 \% & 527 & 1636 & 0.01 a / GPU
\end{tabular}