\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
SRK\_ODESA(car) & on & 90.03 \% & 84.32 \% & 82.62 \% & 2.31 \% & 90 & 501 & 0.4 s / & D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020.\\
CenterTrack & on & 89.44 \% & 85.05 \% & 82.31 \% & 2.31 \% & 116 & 334 & 0.045s / & X. Zhou, V. Koltun and P. Krähenbühl: Tracking Objects as Points. ECCV 2020.\\
RE3T & la on & 88.89 \% & 84.36 \% & 78.92 \% & 10.77 \% & 31 & 193 & 0.0045 s / 1 core & \\
FG-3DMOT & la & 88.01 \% & 85.04 \% & 75.54 \% & 11.85 \% & 20 & 117 & 0.04 s / 4 cores & \\
PorTrack & & 87.63 \% & 84.83 \% & 83.38 \% & 3.08 \% & 387 & 693 & 0.06 s / 1 core & \\
EagerMOT & la on & 87.09 \% & 85.09 \% & 80.62 \% & 2.46 \% & 43 & 545 & 0.011 s / 4 cores & \\
TuSimple & on & 86.62 \% & 83.97 \% & 72.46 \% & 6.77 \% & 293 & 501 & 0.6 s / 1 core & W. Choi: Near-online multi-target tracking with aggregated local flow descriptor. Proceedings of the IEEE International Conference on Computer Vision 2015.K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.\\
VV\_team & la on & 86.10 \% & 84.83 \% & 76.77 \% & 3.54 \% & 439 & 859 & 0.1 s / GPU & \\
Quasi-Dense & on & 85.76 \% & 85.01 \% & 69.08 \% & 3.08 \% & 93 & 617 & 0.07s / & J. Pang, L. Qiu, H. Chen, Q. Li, T. Darrell and F. Yu: Quasi-Dense Instance Similarity Learning. arXiv:2006.06664 2020.\\
JRMOT & la on & 85.70 \% & 85.48 \% & 71.85 \% & 4.00 \% & 98 & 372 & 0.07 s / 4 cores & A. Shenoi, M. Patel, J. Gwak, P. Goebel, A. Sadeghian, H. Rezatofighi, R. Mart\'in-Mart\'in and S. Savarese: JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset. The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
MASS & on & 85.04 \% & 85.53 \% & 74.31 \% & 2.77 \% & 301 & 744 & 0.01s / & H. Karunasekera, H. Wang and H. Zhang: Multiple Object Tracking with attention to Appearance, Structure, Motion and Size. IEEE Access 2019.\\
MOTSFusion & st & 84.83 \% & 85.21 \% & 73.08 \% & 2.77 \% & 275 & 759 & 0.44s / & J. Luiten, T. Fischer and B. Leibe: Track to Reconstruct and Reconstruct to Track. arXiv preprint arXiv:1910.00130 2019.\\
mmMOT & & 84.77 \% & 85.21 \% & 73.23 \% & 2.77 \% & 284 & 753 & 0.02s / GPU & W. Zhang, H. Zhou, Sun, Z. Wang, J. Shi and C. Loy: Robust Multi-Modality Multi-Object Tracking. International Conference on Computer Vision (ICCV) 2019.\\
mono3DT & gp on & 84.52 \% & 85.64 \% & 73.38 \% & 2.77 \% & 377 & 847 & 0.03 s / GPU & H. Hu, Q. Cai, D. Wang, J. Lin, M. Sun, P. Krähenbühl, T. Darrell and F. Yu: Joint Monocular 3D Vehicle Detection and Tracking. ICCV 2019.\\
SMAT & on & 84.27 \% & 86.09 \% & 63.08 \% & 5.38 \% & 28 & 341 & 0.1 s / 1 core & N. Gonzalez, A. Ospina and P. Calvez: SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking. Image Analysis and Recognition 2020.\\
MOTBeyondPixels & on & 84.24 \% & 85.73 \% & 73.23 \% & 2.77 \% & 468 & 944 & 0.3 s / 1 core & S. Sharma, J. Ansari, J. Krishna Murthy and K. Madhava Krishna: Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018.\\
AB3DMOT & la on & 83.84 \% & 85.24 \% & 66.92 \% & 11.38 \% & 9 & 224 & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
IMMDP & on & 83.04 \% & 82.74 \% & 60.62 \% & 11.38 \% & 172 & 365 & 0.19 s / 4 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.S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.\\
MOT\_FLID & & 83.03 \% & 85.21 \% & 69.08 \% & 11.69 \% & 20 & 151 & 0.05 s / 1 core & \\
ST-3D & st & 82.64 \% & 83.83 \% & 61.69 \% & 7.23 \% & 370 & 856 & 0.07 s / 1 core & \\
aUToTrack & la gp on & 82.25 \% & 80.52 \% & 72.62 \% & 3.54 \% & 1025 & 1402 & 0.01 s / 1 core & K. Burnett, S. Samavi, S. Waslander, T. Barfoot and A. Schoellig: aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge. arXiv:1905.08758 2019.\\
GNNMOT & & 82.24 \% & 84.05 \% & 64.92 \% & 6.00 \% & 142 & 416 & 0.01 s / 4 cores & \\
UDOLO & & 81.59 \% & 86.17 \% & 63.08 \% & 6.46 \% & 222 & 875 & 0.15 s / GPU & \\
JCSTD & on & 80.57 \% & 81.81 \% & 56.77 \% & 7.38 \% & 61 & 643 & 0.07 s / 1 core & W. Tian, M. Lauer and L. Chen: Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. IEEE Transactions on Intelligent Transportation Systems 2019.\\
3D-CNN/PMBM & gp on & 80.39 \% & 81.26 \% & 62.77 \% & 6.15 \% & 121 & 613 & 0.01 s / 1 core & S. Scheidegger, J. Benjaminsson, E. Rosenberg, A. Krishnan and K. Granström: Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering. 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, June 26-30, 2018 2018.\\
extraCK & on & 79.99 \% & 82.46 \% & 62.15 \% & 5.54 \% & 343 & 938 & 0.03 s / 1 core & G. Gunduz and T. Acarman: A lightweight online multiple object vehicle tracking method. Intelligent Vehicles Symposium (IV), 2018 IEEE 2018.\\
M^3 tracker & & 79.93 \% & 84.77 \% & 66.00 \% & 10.00 \% & 278 & 716 & 0.02 s / 8 cores & \\
CD & & 79.62 \% & 83.48 \% & 60.31 \% & 5.38 \% & 184 & 701 & 20 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
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.\\
CSFADet-tracker & & 77.73 \% & 84.01 \% & 65.54 \% & 4.00 \% & 883 & 1417 & 0.05 s / GPU & \\
FANTrack & la on & 77.72 \% & 82.33 \% & 62.62 \% & 8.77 \% & 150 & 812 & 0.04 s / 8 cores & E. Baser, V. Balasubramanian, P. Bhattacharyya and K. Czarnecki: FANTrack: 3D Multi-Object Tracking with Feature Association Network. ArXiv 2019.\\
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.\\
FAMNet & & 77.08 \% & 78.79 \% & 51.38 \% & 8.92 \% & 123 & 713 & 1.5 s / GPU & P. Chu and H. Ling: FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. ICCV 2019.\\
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.\\
BLAC & on & 76.40 \% & 83.05 \% & 47.38 \% & 14.00 \% & 147 & 608 & 0.03 s / GPU & \\
DSM & & 76.15 \% & 83.42 \% & 60.00 \% & 8.31 \% & 296 & 868 & 0.1 s / GPU & D. Frossard and R. Urtasun: End-To-End Learning of Multi-Sensor 3D Tracking by Detection. ICRA 2018.\\
Complexer-YOLO & la gp on & 75.70 \% & 78.46 \% & 58.00 \% & 5.08 \% & 1186 & 2092 & 0.01 a / GPU & M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.\\
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.\\
SASN-MCF\_nano & & 70.86 \% & 82.65 \% & 58.00 \% & 7.85 \% & 443 & 975 & 0.02 s / 1 core & G. Gunduz and T. Acarman: Efficient Multi-Object Tracking by Strong Associations on Temporal Window. IEEE Transactions on Intelligent Vehicles 2019.\\
Point3DT & la & 68.24 \% & 76.57 \% & 60.62 \% & 12.31 \% & 111 & 725 & 0.05 s / 1 core & Sukai Wang and M. Liu: PointTrackNet: An End-to-End Network for 3-D Object Detection and Tracking from Point Clouds. to be submitted ICRA'20 .\\
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.\\
BEV-Tracking & la on & 66.02 \% & 85.81 \% & 45.69 \% & 8.46 \% & 1889 & 2578 & 0.03 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.\\
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.\\
SORT & & 54.22 \% & 77.57 \% & 25.69 \% & 29.08 \% & 1 & 557 & .002 s / 1 core & A. Bewley, Z. Ge, L. Ott, F. Ramos and B. Upcroft: Simple online and realtime tracking. 2016 IEEE International Conference on Image Processing (ICIP) 2016.\\
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.\\
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}