\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 & 86.62 \% & 83.97 \% & 72.46 \% & 6.77 \% & 293 & 501 & 0.6 s / 1 core & \\
NECMA & & 84.98 \% & 83.14 \% & 70.77 \% & 9.08 \% & 33 & 162 & 0.5 s / 8 cores & \\
RRC-IIITH & on & 84.24 \% & 85.73 \% & 73.23 \% & 2.77 \% & 468 & 944 & 0.3 s / 1 core & \\
RBPF & & 83.64 \% & 82.25 \% & 64.77 \% & 5.85 \% & 273 & 651 & 1 s / 1 core & \\
IMMDP & on & 83.04 \% & 82.74 \% & 60.62 \% & 11.38 \% & 172 & 365 & 0.19 s / 4 cores & \\
DuEye & on & 80.64 \% & 83.52 \% & 61.85 \% & 5.85 \% & 356 & 991 & 0.15 s / 1 core & \\
JCSTD & on & 80.57 \% & 81.81 \% & 56.77 \% & 7.38 \% & 61 & 643 & 0.11 s / 1 core & \\
MCMOT-CPD & & 78.90 \% & 82.13 \% & 52.31 \% & 11.69 \% & 228 & 536 & 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.\\
NOMT* & & 78.15 \% & 79.46 \% & 57.23 \% & 13.23 \% & 31 & 207 & 0.09 s / 16 cores & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
wan & on & 78.07 \% & 82.83 \% & 51.38 \% & 13.38 \% & 24 & 235 & 0.1 s / 1 core & \\
LP-SSVM* & & 77.63 \% & 77.80 \% & 56.31 \% & 8.46 \% & 62 & 539 & 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.\\
CCF-MOT & on & 77.08 \% & 78.36 \% & 52.62 \% & 13.08 \% & 69 & 391 & 1.1 s / 1 core & \\
MDP & on & 76.59 \% & 82.10 \% & 52.15 \% & 13.38 \% & 130 & 387 & 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.\\
DSM & & 76.15 \% & 83.42 \% & 60.00 \% & 8.31 \% & 296 & 868 & 0.1 s / GPU & \\
SLP* & & 75.79 \% & 78.79 \% & 53.85 \% & 9.54 \% & 59 & 543 & 0.1 s / 1 core & \\
SCEA* & on & 75.58 \% & 79.39 \% & 53.08 \% & 11.54 \% & 104 & 448 & 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.\\
CIWT* & st on & 75.39 \% & 79.25 \% & 49.85 \% & 10.31 \% & 165 & 660 & 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.\\
NOMT-HM* & on & 75.20 \% & 80.02 \% & 50.00 \% & 13.54 \% & 105 & 351 & 0.09 s / 8 cores & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
SSP* & & 72.72 \% & 78.55 \% & 53.85 \% & 8.00 \% & 185 & 932 & 0.6 s / 1 core & P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015.\\
mbodSSP* & on & 72.69 \% & 78.75 \% & 48.77 \% & 8.77 \% & 114 & 858 & 0.01 s / 1 core & P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015.\\
DCO-X* & & 68.11 \% & 78.85 \% & 37.54 \% & 14.15 \% & 318 & 959 & 0.9 s / 1 core & A. Milan, K. Schindler and S. Roth: Detection- and Trajectory-Level Exclusion in Multiple Object Tracking. CVPR 2013.\\
NOMT & & 66.60 \% & 78.17 \% & 41.08 \% & 25.23 \% & 13 & 150 & 0.09 s / 16 core & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
DBHM* & on & 66.22 \% & 77.72 \% & 52.15 \% & 8.15 \% & 1557 & 2060 & 0.15 s / 4 cores & \\
RMOT* & on & 65.83 \% & 75.42 \% & 40.15 \% & 9.69 \% & 209 & 727 & 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 & & 61.77 \% & 76.93 \% & 35.54 \% & 21.69 \% & 16 & 422 & 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.\\
NOMT-HM & on & 61.17 \% & 78.65 \% & 33.85 \% & 28.00 \% & 28 & 241 & 0.09 s / 8 cores & W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.\\
ODAMOT & on & 59.23 \% & 75.45 \% & 27.08 \% & 15.54 \% & 389 & 1274 & 1 s / 1 core & A. Gaidon and E. Vig: Online Domain Adaptation for Multi-Object Tracking. British Machine Vision Conference (BMVC) 2015.\\
SSP & & 57.85 \% & 77.64 \% & 29.38 \% & 24.31 \% & 7 & 704 & 0.6s / 1 core & P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015.\\
SCEA & on & 57.03 \% & 78.84 \% & 26.92 \% & 26.62 \% & 17 & 461 & 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.\\
mbodSSP & on & 56.03 \% & 77.52 \% & 23.23 \% & 27.23 \% & 0 & 699 & 0.01 s / 1 core & P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015.\\
TDCS & on & 55.38 \% & 75.20 \% & 23.23 \% & 21.85 \% & 118 & 961 & 0.06 s / 1 core & \\
TBD & & 55.07 \% & 78.35 \% & 20.46 \% & 32.62 \% & 31 & 529 & 10 s / 1 core & A. Geiger, M. Lauer, C. Wojek, C. Stiller and R. Urtasun: 3D Traffic Scene Understanding from Movable Platforms. Pattern Analysis and Machine Intelligence (PAMI) 2014.H. Zhang, A. Geiger and R. Urtasun: Understanding High-Level Semantics by Modeling Traffic Patterns. International Conference on Computer Vision (ICCV) 2013.\\
RMOT & on & 52.42 \% & 75.18 \% & 21.69 \% & 31.85 \% & 50 & 376 & 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.\\
CEM & & 51.94 \% & 77.11 \% & 20.00 \% & 31.54 \% & 125 & 396 & 0.09 s / 1 core & A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking. IEEE TPAMI 2014.\\
MCF & & 45.92 \% & 78.25 \% & 14.92 \% & 37.23 \% & 21 & 581 & 0.01 s / 1 core & L. Zhang, Y. Li and R. Nevatia: Global data association for multi-object tracking using network flows.. CVPR .\\
HM & on & 43.85 \% & 78.34 \% & 12.46 \% & 39.54 \% & 12 & 571 & 0.01 s / 1 core & A. Geiger: Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms. 2013.\\
FMMOVT V2 & on & 39.40 \% & 80.05 \% & 21.08 \% & 31.08 \% & 585 & 1122 & 0.05 s / 1 core & \\
DP-MCF & & 38.33 \% & 78.41 \% & 18.00 \% & 36.15 \% & 2716 & 3225 & 0.01 s / 1 core & H. Pirsiavash, D. Ramanan and C. Fowlkes: Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. IEEE conference on Computer Vision and Pattern Recognition (CVPR) 2011.\\
DCO & & 37.28 \% & 74.36 \% & 15.54 \% & 30.92 \% & 220 & 612 & 0.03 s / 1 core & A. Andriyenko, K. Schindler and S. Roth: Discrete-Continuous Optimization for Multi-Target Tracking. CVPR 2012.\\
FMMOVT & & 31.88 \% & 77.68 \% & 21.38 \% & 34.92 \% & 511 & 930 & 0.05 s / 1 core & F. Alencar, C. Massera, D. Ridel and D. Wolf: Fast Metric Multi-Object Vehicle Tracking for Dynamical Environment Comprehension. Latin American Robotics Symposium (LARS), 2015 2015.
\end{tabular}