\begin{tabular}{c | c | c | c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\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 \cite{vipdeeplab} & 68.70 \% & 84.50 \% & 82.30 \% & 85.50 \% & 85.50 \% & 93.90 \% & 73.30 \% & 2.60 \% & 209 & 443 & 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.\\
ReMOTS \cite{yang2020remots} & 66.00 \% & 81.30 \% & 82.00 \% & 83.20 \% & 83.20 \% & 94.00 \% & 62.60 \% & 5.60 \% & 391 & 551 & 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.\\
MAF\_HDA \cite{mafhda} & 65.00 \% & 79.60 \% & 82.30 \% & 81.10 \% & 81.10 \% & 94.00 \% & 57.80 \% & 6.30 \% & 300 & 520 & 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.\\
GMPHD\_SAF \cite{gmphdsaf} & 62.80 \% & 78.20 \% & 81.60 \% & 80.40 \% & 80.50 \% & 93.70 \% & 59.30 \% & 4.80 \% & 474 & 696 & 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.\\
PointTrack \cite{xu2020Segment} & 61.50 \% & 76.50 \% & 81.00 \% & 77.40 \% & 77.40 \% & 93.80 \% & 48.90 \% & 9.30 \% & 176 & 632 & 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 \cite{9881968} & 61.00 \% & 75.70 \% & 81.30 \% & 76.90 \% & 76.90 \% & 93.80 \% & 53.00 \% & 8.50 \% & 233 & 707 & 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.\\
SearchTrack \cite{tsai2022searchtrack} & 60.60 \% & 78.90 \% & 78.20 \% & 80.70 \% & 80.80 \% & 93.00 \% & 60.40 \% & 4.40 \% & 390 & 714 & 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.\\
MOTSFusion \cite{luiten2019MOTSFusion} & 58.70 \% & 72.90 \% & 81.50 \% & 74.20 \% & 74.20 \% & 94.10 \% & 47.40 \% & 15.60 \% & 279 & 534 & 0.44 s / 1 core & J. Luiten, T. Fischer and B. Leibe: Track to Reconstruct and Reconstruct to Track. IEEE Robotics and Automation Letters 2020.\\
EagerMOT \cite{Kim21ICRA} & 58.10 \% & 72.00 \% & 81.50 \% & 73.30 \% & 73.30 \% & 94.10 \% & 43.30 \% & 13.70 \% & 270 & 633 & 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.\\
MPNTrackSeg \cite{MPNTrackSeg} & 57.30 \% & 77.00 \% & 76.00 \% & 77.70 \% & 77.70 \% & 91.90 \% & 56.30 \% & 9.60 \% & 162 & 620 & 0.08 s / 8 cores & G. Bras\'o, O. Cetintas and L. Leal-Taix\'e: Multi-Object Tracking and Segmentation Via Neural Message Passing. International Journal of Computer Vision 2022.\\
MG-MOTS \cite{SEONG2023144} & 54.40 \% & 70.80 \% & 78.50 \% & 72.40 \% & 72.50 \% & 93.50 \% & 41.50 \% & 19.60 \% & 351 & 737 & 41 s / GPU & J. Seong: Online and real-time mask-guided multi- person tracking and segmentation. Pattern Recognition Letters 2023.\\
TrackR-CNN \cite{Voigtlaender19CVPRMOTS} & 47.30 \% & 66.10 \% & 74.60 \% & 68.40 \% & 68.40 \% & 91.80 \% & 45.60 \% & 13.30 \% & 481 & 861 & 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 \cite{STCSeg} & 42.60 \% & 57.70 \% & 75.60 \% & 59.60 \% & 59.60 \% & 92.60 \% & 27.80 \% & 18.50 \% & 408 & 780 & 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}