\begin{tabular}{c | c | c | c | c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf sMOTSA} & {\bf MOTSA} & {\bf MOTSP} & {\bf MOTSAL} & {\bf MODSA} & {\bf MODSP} & {\bf MT} & {\bf ML} & {\bf IDS} & {\bf Frag} & {\bf Runtime} & {\bf Environment}\\ \hline
ViP-DeepLab & & 81.00 \% & 90.70 \% & 89.90 \% & 91.80 \% & 91.80 \% & 92.20 \% & 92.20 \% & 0.60 \% & 392 & 580 & 0.1 s / 1 core & S. Qiao, Y. Zhu, H. Adam, A. Yuille and L. Chen: ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2021.\\
PointTrack & & 78.50 \% & 90.90 \% & 87.10 \% & 91.80 \% & 91.80 \% & 89.70 \% & 90.80 \% & 0.60 \% & 346 & 645 & 0.045 s / GPU & Z. Xu, W. Zhang, X. Tan, W. Yang, H. Huang, S. Wen, E. Ding and L. Huang: Segment as Points for Efficient Online Multi-Object Tracking and Segmentation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
OPITrack & & 78.00 \% & 90.40 \% & 87.20 \% & 91.80 \% & 91.80 \% & 89.70 \% & 91.30 \% & 0.80 \% & 542 & 832 & 0.09 s / 1 core & Y. Gao, H. Xu, Y. Zheng, J. Li and X. Gao: An Object Point Set Inductive Tracker for Multi-Object Tracking and Segmentation. IEEE Transactions on Image Processing 2022.\\
MAF\_HDA & on & 77.20 \% & 87.70 \% & 88.40 \% & 88.90 \% & 88.90 \% & 90.90 \% & 82.00 \% & 0.80 \% & 415 & 706 & 0.09 s / 4 cores & Y. Song, Y. Yoon, K. Yoon and M. Jeon: Multi-Object Tracking and Segmentation with Embedding Mask-based Affinity Fusion in Hierarchical Data Association. IEEE Access 2022.\\
ReMOTS & & 75.90 \% & 86.70 \% & 88.20 \% & 88.70 \% & 88.70 \% & 90.70 \% & 84.50 \% & 0.60 \% & 716 & 905 & 3 s / 1 core & F. Yang, X. Chang, C. Dang, Z. Zheng, S. Sakti, S. Nakamura and Y. Wu: ReMOTS: Self-Supervised Refining Multi- Object Tracking and Segmentation. 2020.\\
GMPHD\_SAF & on & 75.40 \% & 86.70 \% & 87.50 \% & 88.20 \% & 88.20 \% & 90.10 \% & 82.00 \% & 0.60 \% & 549 & 874 & 0.08 s / 4 cores & Y. Song and M. Jeon: Online Multi-Object Tracking and Segmentation with GMPHD Filter and Simple Affinity Fusion. arXiv preprint arXiv:2009.00100 2020.\\
MOTSFusion & & 75.00 \% & 84.10 \% & 89.30 \% & 84.70 \% & 84.70 \% & 91.70 \% & 66.10 \% & 6.20 \% & 201 & 572 & 0.44 s / GPU & J. Luiten, T. Fischer and B. Leibe: Track to Reconstruct and Reconstruct to Track. IEEE Robotics and Automation Letters 2020.\\
SearchTrack & & 74.80 \% & 86.80 \% & 86.80 \% & 88.50 \% & 88.50 \% & 89.70 \% & 80.00 \% & 1.50 \% & 614 & 983 & 0.19 s / GPU & Z. Tsai, Y. Tsai, C. Wang, H. Liao, Y. Lin and Y. Chuang: SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features. BMVC 2022.\\
EagerMOT & & 74.50 \% & 83.50 \% & 89.60 \% & 84.80 \% & 84.80 \% & 92.10 \% & 67.10 \% & 3.50 \% & 457 & 811 & 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.\\
TrackR-CNN & & 67.00 \% & 79.60 \% & 85.10 \% & 81.50 \% & 81.50 \% & 88.30 \% & 74.90 \% & 2.30 \% & 692 & 1058 & 0.5 s / GPU & P. Voigtlaender, M. Krause, A. O\usep, J. Luiten, B. Sekar, A. Geiger and B. Leibe: MOTS: Multi-Object Tracking and Segmentation. CVPR 2019.\\
STC-Seg & & 66.20 \% & 81.10 \% & 82.80 \% & 82.90 \% & 82.90 \% & 86.30 \% & 71.90 \% & 2.00 \% & 676 & 1093 & 0.25 s / 1 core & Y. Liqi, W. Qifan, M. Siqi, W. Jingang and C. Yu: Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2022.
\end{tabular}