\begin{tabular}{c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf MOTA} & {\bf MOTP} & {\bf MT} & {\bf ML} & {\bf IDS} & {\bf FRAG} & {\bf Runtime} & {\bf Environment}\\ \hline
TuSimple & on & 58.15 \% & 71.93 \% & 30.58 \% & 24.05 \% & 138 & 818 & 0.6 s / 1 core & \\
ET-MOT & on & 51.44 \% & 72.65 \% & 25.43 \% & 18.21 \% & 396 & 1405 & 0.7 s / GPU & \\
MDP & on & 47.22 \% & 70.36 \% & 24.05 \% & 27.84 \% & 87 & 825 & 0.9 s / 8 cores & Y. Xiang, A. Alahi and S. Savarese: Learning to Track: Online Multi- Object Tracking by Decision Making. International Conference on Computer Vision (ICCV) 2015.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.\\
NOMT* & & 46.62 \% & 71.45 \% & 26.12 \% & 34.02 \% & 63 & 666 & 0.09 s / 16 cores & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
MCMOT-CPD & & 45.94 \% & 72.44 \% & 20.62 \% & 34.36 \% & 143 & 764 & 0.01 s / 1 core & B. Lee, E. Erdenee, S. Jin, M. Nam, Y. Jung and P. Rhee: Multi-class Multi-object Tracking Using Changing Point Detection. ECCVWORK 2016.\\
CCF-MOT & on & 44.52 \% & 68.38 \% & 24.40 \% & 37.11 \% & 211 & 976 & 1.1 s / 1 core & \\
JCSTD & on & 44.20 \% & 72.09 \% & 16.49 \% & 33.68 \% & 53 & 917 & 0.11 s / 1 core & \\
SCEA* & on & 43.91 \% & 71.86 \% & 16.15 \% & 43.30 \% & 56 & 641 & 0.06 s / 1 core & J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via Structural Constraint Event Aggregation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016.\\
RMOT* & on & 43.77 \% & 71.02 \% & 19.59 \% & 41.24 \% & 153 & 748 & 0.02 s / 1 core & J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. IEEE Winter Conference on Applications of Computer Vision (WACV) 2015.\\
LP-SSVM* & & 43.76 \% & 70.48 \% & 20.62 \% & 34.36 \% & 73 & 809 & 0.02 s / 1 core & S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016.\\
CIWT* & st on & 43.37 \% & 71.44 \% & 13.75 \% & 34.71 \% & 112 & 901 & 0.28 s / 1 core & A. Osep, W. Mehner, M. Mathias and B. Leibe: Combined Image- and World-Space Tracking in Traffic Scenes. ICRA 2017.\\
NECMA & & 42.67 \% & 72.51 \% & 30.58 \% & 39.18 \% & 49 & 529 & 0.5 s / 8 cores & \\
NOMT-HM* & on & 39.26 \% & 71.14 \% & 21.31 \% & 41.92 \% & 184 & 863 & 0.09 s / 8 cores & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
LXT-MOT & & 39.16 \% & 72.31 \% & 14.43 \% & 35.40 \% & 233 & 905 & 0.3 s / GPU & \\
NOMT & & 36.93 \% & 67.75 \% & 17.87 \% & 42.61 \% & 34 & 789 & 0.09 s / 16 core & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
RMOT & on & 34.54 \% & 68.06 \% & 14.43 \% & 47.42 \% & 81 & 685 & 0.01 s / 1 core & J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. IEEE Winter Conference on Applications of Computer Vision (WACV) 2015.\\
LP-SSVM & & 33.33 \% & 67.38 \% & 12.37 \% & 45.02 \% & 72 & 818 & 0.05 s / 1 core & S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016.\\
SCEA & on & 33.13 \% & 68.45 \% & 9.62 \% & 46.74 \% & 16 & 717 & 0.05 s / 1 core & J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via Structural Constraint Event Aggregation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016.\\
CEM & & 27.54 \% & 68.48 \% & 8.93 \% & 51.89 \% & 96 & 608 & 0.09 s / 1 core & A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking. IEEE TPAMI 2014.\\
NOMT-HM & on & 27.49 \% & 67.99 \% & 15.12 \% & 50.52 \% & 73 & 732 & 0.09 s / 8 cores & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
\end{tabular}