Dataset

• MOT Challenge

• ImageNet VID

Metrics

• • FP: False Positive

  • FN: False Negative

  • ID Switches

  • MOTA: Multiple Object Tracking Accuracy

  • MOTP: Multiple Object Tracking Precision

  • MT: Most Tracked Target

  • ML: Most Lost Target

  • Hz (FPs): Tracking Speed 

• K. Bernardin and R. Stiefelhagen, "Evaluating multiple object tracking performance: the clear mot metrics," in EURASIP Journal on Image and Video Processing, pp. 1–10, 2008.

Tracking-by-Detection (TBD)

1. [SORT] A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Realtime Tracking", in IEEE International Conference on Image Processing (ICIP), 2016.

2. [DeepSORT] N. Wojke, A. Bewley, and D. Paulus, "Simple Online and Realtime Tracking with a Deep Association Metric", in IEEE International Conference on Image Processing (ICIP), 2017.

3. [FairMOT] Y. Zhang, C. Wang, X. Wang, W. Zeng, and W. Liu, "FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking", in IJCV2021.

4. [ByteTrack] Y Zhang, P Sun, Y Jiang, D Yu, Z Yuan, P Luo, W Liu, X Wang, "ByteTrack: Multi-Object Tracking by Associating Every Detection Box", in arXiv:2110.06864, 2021. 

5. [StrongSORT] Y Du, Y Song, B Yang, Y Zhao, "StrongSORT: Make DeepSORT Great Again", in arXiv preprint arXiv:2202.13514, 2022.

6. [BoT-SORT] "BoT-SORT: Robust Associations Multi-Pedestrian Tracking"

Joint detection and tracking (JDT)

• • [Tracktor] P. Bergmann, T. Meinhardt, and L. Leal-Taixé, “Tracking without bells and whistles” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 941–951, 2019.

  • [JDE] Z. Wang, L. Zheng, Y. Liu, Y. Li, and S. Wang, “Towards real-time multi-object tracking”, in ECCV 2020, pp. 107–122, 2020. 

  • [CenterTrack] X. Zhou, V. Koltun, and P. Krähenbühl, “Tracking objects as points”, in ECCV 2020, pp. 474–490, 2020. 

  • [PermaTrack] P. Tokmakov, J. Li, W. Burgard, and A. Gaidon, “Learning to track with object permanence”, in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10840–10849, 2021.

Transformer-based

• • [TransTrack] P. Sun, J. Cao, Y. Jiang, R. Zhang, E. Xie, Z. Yuan, C. Wang, and P. Luo, “TransTrack: Multiple object tracking with Transformer”, in arXiv Preprint, arXiv:2012.15460, 2020.

  • [TrackFormer] T. Meinhardt, A. Kirillov, L. Leal-Taixe, and C. Feichtenhofer, “TrackFormer: Multi-object tracking with Transformers”, in arXiv Preprint, arXiv:2101.02702, 2021.

Unfilter

• • B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, and J. Yan, "SiamRPN++: Evolution of siamese visual tracking with very deep networks," in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4282-4291, 2019.

  • L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr, "Fully-convolutional siamese networks for object tracking," in European Conference on Computer Vision, pp. 850-865, 2016.

  • B. Yan, Y. Jiang, P. Sun, D. Wang, Z. Yuan, P. Luo, and H. Lu, "Towards Grand Unification of Object Tracking", in ECCV, 2022. [Code]

  • "FEAR: Fast, Efficient, Accurate and Robust Visual Tracker"

  • [MLT] Y. Zhang, H. Sheng, Y. Wu, S. Wang, W. Ke, and Z. Xiong. Multiplex labeling graph for near-online tracking in crowded scenes. IEEE Internet of Things Journal, 7(9):7892–7902, 2020

  • [Tube_TK] B. Pang, Y. Li, Y. Zhang, M. Li, and C. Lu. Tubetk: Adopting tubes to track multi-object in a one-step training model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6308–6318, 2020. 

  • [MOTR] F. Zeng, B. Dong, T. Wang, C. Chen, X. Zhang, and Y. Wei. Motr: End-to-end multiple-object tracking with transformer. arXiv preprint arXiv:2105.03247, 2021. 

  • [CTracker] J. Peng, C. Wang, F. Wan, Y. Wu, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, and Y. Fu. Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. In European Conference on Computer Vision, pages 145–161. Springer, 2020.  

  • [CenterTrack] X. Zhou, V. Koltun, and P. Krahenb ¨ uhl. Tracking objects as ¨ points. In European Conference on Computer Vision, pages 474–490. Springer, 2020. 

  • [QuasiDense] J. Pang, L. Qiu, X. Li, H. Chen, Q. Li, T. Darrell, and F. Yu. Quasi-dense similarity learning for multiple object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 164–173, 2021. 

  • [TraDes] J. Wu, J. Cao, L. Song, Y. Wang, M. Yang, and J. Yuan. Track to detect and segment: An online multi-object tracker. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12352–12361, 2021.  

  • [MAT] S. Han, P. Huang, H. Wang, E. Yu, D. Liu, and X. Pan. Mat: Motion-aware multi-object tracking. Neurocomputing, 2022. 

  • [SOTMOT] L. Zheng, M. Tang, Y. Chen, G. Zhu, J. Wang, and H. Lu. Improving multiple object tracking with single object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2453–2462, 2021. 

  • [TransCenter] Y. Xu, Y. Ban, G. Delorme, C. Gan, D. Rus, and X. AlamedaPineda. Transcenter: Transformers with dense queries for multiple-object tracking. arXiv preprint arXiv:2103.15145, 2021. 

  • [GSDT] Y. Wang, K. Kitani, and X. Weng. Joint object detection and multi-object tracking with graph neural networks. arXiv preprint arXiv:2006.13164, 2020. 

  • [Semi-TCL] W. Li, Y. Xiong, S. Yang, M. Xu, Y. Wang, and W. Xia. Semi-tcl: Semi-supervised track contrastive representation learning. arXiv preprint arXiv:2107.02396, 2021. 

  • [FairMOT] Y. Zhang, C. Wang, X. Wang, W. Zeng, and W. Liu. Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of Computer Vision, 129(11):3069–3087, 2021. 

  • [RelationTrack] E. Yu, Z. Li, S. Han, and H. Wang. Relationtrack: Relationaware multiple object tracking with decoupled representation. arXiv preprint arXiv:2105.04322, 2021.

  • [PermaTrackPr] P. Tokmakov, J. Li, W. Burgard, and A. Gaidon. Learning to track with object permanence. arXiv preprint arXiv:2103.14258, 2021. 

  • [CSTrack] C. Liang, Z. Zhang, Y. Lu, X. Zhou, B. Li, X. Ye, and J. Zou. Rethinking the competition between detection and reid in multi-object tracking. arXiv preprint arXiv:2010.12138, 2020. 

  • [TransTrack] P. Sun, Y. Jiang, R. Zhang, E. Xie, J. Cao, X. Hu, T. Kong, Z. Yuan, C. Wang, and P. Luo. Transtrack: Multiple-object tracking with transformer. arXiv preprint arXiv:2012.15460, 2020. 

  • [FUFET] C. Shan, C. Wei, B. Deng, J. Huang, X.-S. Hua, X. Cheng, and K. Liang. Tracklets predicting based adaptive graph tracking. arXiv preprint arXiv:2010.09015, 2020. 

  • [SiamMOT] C. Liang, Z. Zhang, X. Zhou, B. Li, Y. Lu, and W. Hu. One more check: Making” fake background” be tracked again. arXiv preprint arXiv:2104.09441, 2021.  

  • [CorrTracker] Q. Wang, Y. Zheng, P. Pan, and Y. Xu. Multiple object tracking with correlation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3876–3886, 2021. 

  • [TransMOT] P. Chu, J. Wang, Q. You, H. Ling, and Z. Liu. Transmot: Spatial-temporal graph transformer for multiple object tracking. arXiv preprint arXiv:2104.00194, 2021.  

  • [ReMOT] F. Yang, X. Chang, S. Sakti, Y. Wu, and S. Nakamura. Remot: A model-agnostic refinement for multiple object tracking. Image and Vision Computing, 106:104091, 2021. 

  • [MAATrack] D. Stadler and J. Beyerer. Modelling ambiguous assignments for multi-person tracking in crowds. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 133–142, 2022. 

  • [OCSORT] J. Cao, X. Weng, R. Khirodkar, J. Pang, and K. Kitani. Observation-centric sort: Rethinking sort for robust multiobject tracking. arXiv preprint arXiv:2203.14360, 2022. 

  • [StrongSORT++] Y. Du, Y. Song, B. Yang, and Y. Zhao. Strongsort: Make deepsort great again. arXiv preprint arXiv:2202.13514, 2022. 

  • [ByteTrack] Y. Zhang, P. Sun, Y. Jiang, D. Yu, Z. Yuan, P. Luo, W. Liu, and X. Wang. Bytetrack: Multi-object tracking by associating every detection box. arXiv preprint arXiv:2110.06864, 2021.