\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
FusionTrack & & 92.62 \% & 86.68 \% & 91.69 \% & 1.85 \% & 26 & 87 & 0.1 s / 1 core & \\
CrossTracker & & 92.05 \% & 87.26 \% & 85.08 \% & 2.62 \% & 56 & 195 & 0.05 s / 1 core & \\
CollabMOT & st & 92.02 \% & 85.78 \% & 86.77 \% & 2.31 \% & 134 & 330 & 0.05 s / 1 core & \\
CasTrack & la & 91.93 \% & 86.19 \% & 86.77 \% & 4.00 \% & 21 & 107 & 0.1 s / 1 core & H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE TGRS 2022.H. Wu, W. Han, C. Wen, X. Li and C. Wang: 3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association. IEEE TITS 2021.\\
PermaTrack & on & 91.92 \% & 85.83 \% & 86.77 \% & 2.31 \% & 138 & 345 & 0.1 s / GPU & P. Tokmakov, J. Li, W. Burgard and A. Gaidon: Learning to Track with Object Permanence. ICCV 2021.\\
CollabMOT & st & 91.88 \% & 85.86 \% & 86.92 \% & 2.46 \% & 248 & 372 & 0.02 s / 4 cores & P. Ninh and H. Kim: CollabMOT Stereo Camera Collaborative Multi Object Tracking. IEEE Access 2024.\\
CollabMOT & st & 91.79 \% & 85.87 \% & 86.92 \% & 2.46 \% & 248 & 375 & 0.01 s / 1 core & \\
PC-TCNN & la & 91.75 \% & 86.17 \% & 87.54 \% & 2.92 \% & 26 & 118 & 0.3 s / & H. Wu, Q. Li, C. Wen, X. Li, X. Fan and C. Wang: Tracklet Proposal Network for Multi-Object Tracking on Point Clouds. IJCAI 2021.\\
RAM & on & 91.73 \% & 85.90 \% & 87.08 \% & 2.31 \% & 255 & 380 & 0.09 s / GPU & P. Tokmakov, A. Jabri, J. Li and A. Gaidon: Object Permanence Emerges in a Random Walk along Memory. ICML 2022.\\
BiTrack & la & 91.72 \% & 87.49 \% & 86.31 \% & 5.23 \% & 23 & 243 & 0.1 s / 1 core & \\
Rethink MOT & & 91.47 \% & 85.63 \% & 89.38 \% & 4.31 \% & 72 & 180 & 0.3 s / 4 cores & L. Wang, J. Zhang, P. Cai and X. Li: Towards Robust Reference System for Autonomous Driving: Rethinking 3D MOT. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA) 2023.\\
PMTrack & & 91.16 \% & 86.87 \% & 87.38 \% & 6.62 \% & 35 & 89 & 0.02 s / 1 core & \\
McByte & & 91.05 \% & 85.71 \% & 80.15 \% & 4.00 \% & 85 & 151 & 99 min / GPU & ERROR: Wrong syntax in BIBTEX file.\\
RobMOT & & 91.04 \% & 86.56 \% & 83.54 \% & 10.15 \% & 25 & 71 & 1 s / 1 core & M. Nagy, N. Werghi, B. Hassan, J. Dias and M. Khonji: RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud. 2024.\\
DFR & & 90.98 \% & 86.55 \% & 83.85 \% & 10.00 \% & 18 & 66 & 0.01 s / 1 core & \\
SCMOT & & 90.90 \% & 86.31 \% & 84.46 \% & 5.85 \% & 130 & 197 & 0.01 s / 2 cores & \\
LEGO & la on & 90.80 \% & 86.75 \% & 87.69 \% & 1.54 \% & 173 & 246 & 0.01 s / 1 core & Z. Zhang, J. Liu, Y. Xia, T. Huang, Q. Han and H. Liu: LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds. arXiv preprint arXiv:2308.09908 2023.\\
OC-SORT & on & 90.64 \% & 85.71 \% & 81.23 \% & 2.92 \% & 225 & 471 & 0.03 s / 1 core & J. Cao, X. Weng, R. Khirodkar, J. Pang and K. Kitani: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. 2022.\\
PNAS-MOT & & 90.42 \% & 85.62 \% & 86.77 \% & 2.31 \% & 552 & 762 & 0.01 s / GPU & C. Peng, Z. Zeng, J. Gao, J. Zhou, M. Tomizuka, X. Wang, C. Zhou and N. Ye: PNAS-MOT: Multi-Modal Object Tracking With Pareto Neural Architecture Search. IEEE Robotics and Automation Letters 2024.\\
Anonymous & la on & 90.37 \% & 87.01 \% & 81.69 \% & 8.31 \% & 24 & 372 & 0.01 s / 1 core & \\
VirConvTrack & & 90.28 \% & 86.93 \% & 83.23 \% & 11.69 \% & 12 & 66 & 0.1 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
SRK\_ODESA(mc) & 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.\\
FusionTrack+pointgnn & & 89.67 \% & 85.57 \% & 76.77 \% & 3.85 \% & 26 & 316 & 0.1 s / 1 core & \\
CollabMOT & st & 89.60 \% & 85.04 \% & 82.31 \% & 2.31 \% & 123 & 331 & 0.05 s / 1 core & P. Ninh and H. Kim: CollabMOT Stereo Camera Collaborative Multi Object Tracking. IEEE Access 2024.\\
CollabMOT & st & 89.46 \% & 85.05 \% & 82.31 \% & 2.31 \% & 118 & 330 & 0.05 s / 1 core & \\
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.\\
APPTracker & on & 89.44 \% & 85.15 \% & 78.62 \% & 3.85 \% & 125 & 415 & 0.04 s / GPU & \\
S3Track & & 88.97 \% & 87.25 \% & 86.92 \% & 1.69 \% & 154 & 369 & 0.03 s / 1 core & Anonymous: S$^3$Track: Self-supervised Tracking with Soft Assignment Flow. .\\
DEFT & on & 88.95 \% & 84.55 \% & 84.77 \% & 1.85 \% & 343 & 553 & 0.04 s / GPU & M. Chaabane, P. Zhang, R. Beveridge and S. O'Hara: DEFT: Detection Embeddings for Tracking. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021.\\
PC3T & la & 88.88 \% & 84.37 \% & 80.00 \% & 8.31 \% & 208 & 369 & 0.0045 s / 1 core & H. Wu, W. Han, C. Wen, X. Li and C. Wang: 3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association. IEEE TITS 2021.\\
Mono\_3D\_KF & gp on & 88.77 \% & 83.95 \% & 80.46 \% & 3.69 \% & 96 & 218 & 0.3 s / 1 core & A. Reich and H. Wuensche: Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks. 2021 IEEE 24th International Conference on Information Fusion (FUSION) 2021.\\
SRK\_ODESA(hc) & on & 88.65 \% & 85.70 \% & 78.92 \% & 2.15 \% & 133 & 582 & 0.4 s / GPU & D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020.\\
EagerMOT & & 88.21 \% & 85.73 \% & 76.62 \% & 2.46 \% & 121 & 474 & 0.011 s / 4 cores & A. Kim, A. Osep and L. Leal-Taix'e: EagerMOT: 3D Multi-Object Tracking via Sensor Fusion. IEEE International Conference on Robotics and Automation (ICRA) 2021.\\
MSA-MOT & la on & 88.19 \% & 85.47 \% & 87.23 \% & 1.23 \% & 56 & 405 & 0.01 s / 1 core & Z. Zhu, J. Nie, H. Wu, Z. He and M. Gao: MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking. Sensors 2022.\\
UG3DMOT & & 88.10 \% & 86.58 \% & 79.23 \% & 5.38 \% & 5 & 330 & 0.1 s / 1 core & J. He, C. Fu and X. Wang: 3D Multi-Object Tracking Based on Uncertainty-Guided Data Association. arXiv preprint arXiv:2303.01786 2023.\\
LGM & & 88.06 \% & 84.16 \% & 85.54 \% & 2.15 \% & 469 & 590 & 0.08 s / GPU & G. Wang, R. Gu, Z. Liu, W. Hu, M. Song and J. Hwang: Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.\\
TrackMPNN & on & 87.74 \% & 84.55 \% & 84.77 \% & 1.85 \% & 404 & 607 & 0.05 s / 4 cores & A. Rangesh, P. Maheshwari, M. Gebre, S. Mhatre, V. Ramezani and M. Trivedi: TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking. arXiv preprint arXiv:2101.04206 .\\
SSL3DMOT & & 87.36 \% & 87.64 \% & 74.31 \% & 6.31 \% & 33 & 343 & 3 s / GPU & \\
CMSSL3DMOT & & 87.31 \% & 87.68 \% & 73.23 \% & 6.62 \% & 21 & 331 & 268 s / 1 core & \\
Rt\_Track & & 87.14 \% & 82.72 \% & 69.08 \% & 5.23 \% & 183 & 486 & 0.1 s / 1 core & \\
Stereo3DMOT & & 87.13 \% & 85.17 \% & 75.85 \% & 9.38 \% & 19 & 533 & 0.06 s / 1 core & C. Mao, C. Tan, H. Liu, J. Hu and M. Zheng: Stereo3DMOT: Stereo Vision Based 3D Multi-object Tracking with Multimodal ReID. Chinese Conference on Pattern Recognition and Computer Vision (PRCV) 2023.\\
Stereo3DMOT & st on & 87.13 \% & 85.17 \% & 75.85 \% & 9.38 \% & 19 & 533 & 0.06 s / 1 core & \\
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.\\
MHF-SOE & on & 86.61 \% & 81.65 \% & 73.38 \% & 4.00 \% & 142 & 444 & 1247 s / 1 core & \\
YONTD-MOTv2 & st la on & 86.57 \% & 86.11 \% & 84.92 \% & 2.00 \% & 54 & 334 & 0.1 s / GPU & X. Wang, J. He, C. Fu, T. Meng and M. Huang: You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking. arXiv preprint arXiv:2304.08709 2023.\\
BcMODT & & 86.53 \% & 85.37 \% & 78.31 \% & 2.62 \% & 45 & 626 & 0.01 s / GPU & K. Zhang, Y. Liu, F. Mei, J. Jin and Y. Wang: Boost Correlation Features with 3D-MiIoU- Based Camera-LiDAR Fusion for MODT in Autonomous Driving. Remote Sensing 2023.\\
QD-3DT & on & 86.41 \% & 85.82 \% & 75.38 \% & 2.46 \% & 108 & 553 & 0.03 s / GPU & H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.\\
JMODT & la on & 86.27 \% & 85.41 \% & 77.38 \% & 2.92 \% & 45 & 585 & 0.01 s / GPU & K. Huang and Q. Hao: Joint multi-object detection and tracking with camera-LiDAR fusion for autonomous driving. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.\\
P3DTrack & & 86.06 \% & 84.71 \% & 75.38 \% & 4.31 \% & 230 & 384 & 0.1 s / GPU & \\
AIPT & & 85.91 \% & 85.42 \% & 66.77 \% & 6.62 \% & 42 & 460 & 0.5 s / 1 core & \\
Quasi-Dense & on & 85.76 \% & 85.01 \% & 69.08 \% & 3.08 \% & 93 & 617 & 0.07s / & J. Pang, L. Qiu, X. Li, H. Chen, Q. Li, T. Darrell and F. Yu: Quasi-Dense Similarity Learning for Multiple Object Tracking. CVPR 2021.\\
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.\\
StrongFusion-MOT & & 85.63 \% & 85.17 \% & 66.15 \% & 6.00 \% & 34 & 399 & 0.01 s / 8 cores & X. Wang, C. Fu, J. He, S. Wang and J. Wang: StrongFusionMOT: A Multi-Object Tracking Method Based on LiDAR-Camera Fusion. IEEE Sensors Journal 2022.\\
RA3DMOT & & 85.56 \% & 87.19 \% & 83.38 \% & 1.85 \% & 57 & 622 & 0.01 s / GPU & \\
PolarMOT & & 85.31 \% & 85.52 \% & 81.38 \% & 2.31 \% & 408 & 900 & 0.02 s / 1 core & A. Kim, G. Bras'o, A. O\vsep and L. Leal-Taix'e: PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?. European Conference on Computer Vision (ECCV) 2022.\\
YONTD-MOT & st la on & 85.19 \% & 87.10 \% & 67.54 \% & 7.08 \% & 21 & 342 & 0.1 s / GPU & X. Wang, J. He, C. Fu, T. Meng and M. Huang: You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking. arXiv preprint arXiv:2304.08709 2023.\\
3DMLA & & 85.12 \% & 84.91 \% & 70.62 \% & 5.85 \% & 15 & 318 & 0.02 s / 1 core & M. Cho and E. Kim: 3D LiDAR Multi-Object Tracking with Short-Term and Long-Term Multi-Level Associations. Remote Sensing 2023.\\
EAFFMOT & la on & 85.04 \% & 85.13 \% & 70.92 \% & 8.31 \% & 15 & 256 & 0.01 s / 1 core & J. Jin, J. Zhang, K. Zhang, Y. Wang, Y. Ma and D. Pan: 3D multi-object tracking with boosting data association and improved trajectory management mechanism. Signal Processing 2024.\\
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. IEEE Robotics and Automation Letters 2020.\\
DeepFusion-MOT & st la on & 84.80 \% & 85.10 \% & 68.46 \% & 9.08 \% & 35 & 444 & 0.01 s / >8 cores & X. Wang, C. Fu, Z. Li, Y. Lai and J. He: DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association. IEEE Robotics and Automation Letters 2022.\\
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.\\
TripletTrack & & 84.77 \% & 86.16 \% & 69.54 \% & 3.38 \% & 222 & 646 & 0.1 s / 1 core & N. Marinello, M. Proesmans and L. Van Gool: TripletTrack: 3D Object Tracking Using Triplet Embeddings and LSTM. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022.\\
FNC2 & la on & 84.75 \% & 85.80 \% & 76.00 \% & 5.85 \% & 33 & 311 & 0.01 s / 1 core & C. Jiang, Z. Wang, H. Liang and Y. Wang: A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking. IEEE Transactions on Intelligent Vehicles 2024.C. Jiang, Z. Wang and H. Liang: A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection. IEEE Sensors Journal 2022.\\
DiTMOT & & 84.73 \% & 84.40 \% & 74.92 \% & 12.92 \% & 31 & 188 & 0.08 s / 1 core & S. Wang, P. Cai, L. Wang and M. Liu: DiTNet: End-to-End 3D Object Detection and Track ID Assignment in Spatio-Temporal World. IEEE Robotics and Automation Letters 2021.\\
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+PointRCNN & & 83.92 \% & 85.30 \% & 66.77 \% & 9.08 \% & 10 & 199 & 0.0047s / 1 core & X. Weng, J. Wang, D. Held and K. Kitani: 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics. IROS 2020.\\
MO-YOLO & & 83.55 \% & 84.61 \% & 72.00 \% & 5.23 \% & 252 & 569 & 0.024 s / & L. Pan, Y. Feng, W. Di, L. Bo and Z. Xingle: MO-YOLO: End-to-End Multiple-Object Tracking Method with YOLO and MOTR. arXiv preprint arXiv:2310.17170 2023.\\
3DMAETracking & la & 83.20 \% & 85.31 \% & 62.77 \% & 7.38 \% & 86 & 277 & 34 s / >8 cores & \\
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.\\
SUAMOT & st la & 82.48 \% & 85.51 \% & 56.92 \% & 9.54 \% & 24 & 627 & 0.01 s / 8 cores & \\
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.\\
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.\\
NC2 & la on & 78.95 \% & 85.82 \% & 76.00 \% & 5.69 \% & 31 & 275 & 0.01 s / 1 core & C. Jiang, Z. Wang, H. Liang and Y. Wang: A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking. IEEE Transactions on Intelligent Vehicles 2024.\\
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.\\
MC\_CATrack & on & 78.78 \% & 79.86 \% & 52.15 \% & 11.38 \% & 49 & 324 & 0.05 s / GPU & \\
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.\\
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.\\
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.\\
SST [st] & st & 67.38 \% & 83.98 \% & 43.08 \% & 20.15 \% & 13 & 212 & 1 s / 1 core & \\
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.\\
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.\\
PESORT & & 28.86 \% & 83.96 \% & 22.62 \% & 60.00 \% & 16 & 58 & 0.04 s / GPU &
\end{tabular}