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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking (2312.01650v2)

Published 4 Dec 2023 in cs.CV

Abstract: We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack.

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References (33)
  1. D. Reid, “An algorithm for tracking multiple targets,” IEEE transactions on Automatic Control, vol. 24, no. 6, pp. 843–854, 1979.
  2. T. Fortmann, Y. Bar-Shalom, and M. Scheffe, “Sonar tracking of multiple targets using joint probabilistic data association,” IEEE journal of Oceanic Engineering, vol. 8, no. 3, pp. 173–184, 1983.
  3. B.-N. Vo, B.-T. Vo, and H. G. Hoang, “An efficient implementation of the generalized labeled multi-bernoulli filter,” IEEE Transactions on Signal Processing, vol. 65, no. 8, pp. 1975–1987, 2016.
  4. D. Y. Kim, B.-N. Vo, B.-T. Vo, and M. Jeon, “A labeled random finite set online multi-object tracker for video data,” Pattern Recognition, vol. 90, pp. 377–389, 2019.
  5. M. Abbaspour and M. A. Masnadi-Shirazi, “Online multi-object tracking with δ𝛿\deltaitalic_δ-glmb filter based on occlusion and identity switch handling,” Image and Vision Computing, vol. 127, p. 104553, 2022.
  6. N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in 2017 IEEE international conference on image processing (ICIP).   IEEE, 2017, pp. 3645–3649.
  7. H. W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.
  8. R. Jonker and T. Volgenant, “A shortest augmenting path algorithm for dense and sparse linear assignment problems,” in DGOR/NSOR: Papers of the 16th Annual Meeting of DGOR in Cooperation with NSOR/Vorträge der 16. Jahrestagung der DGOR zusammen mit der NSOR.   Springer, 1988, pp. 622–622.
  9. 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, vol. 129, pp. 3069–3087, 2021.
  10. Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “Yolox: Exceeding yolo series in 2021,” arXiv preprint arXiv:2107.08430, 2021.
  11. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
  12. Z. Wang, L. Zheng, Y. Liu, Y. Li, and S. Wang, “Towards real-time multi-object tracking,” in European Conference on Computer Vision.   Springer, 2020, pp. 107–122.
  13. 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,” in European Conference on Computer Vision.   Springer, 2022, pp. 1–21.
  14. A. Milan, L. Leal-Taixé, I. Reid, S. Roth, and K. Schindler, “MOT16: A benchmark for multi-object tracking,” arXiv preprint arXiv:1603.00831, 2016.
  15. W. A. El Ahmar, D. Kolhatkar, F. E. Nowruzi, H. AlGhamdi, J. Hou, and R. Laganiere, “Multiple object detection and tracking in the thermal spectrum,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 277–285.
  16. S. Stalder, H. Grabner, and L. Van Gool, “Cascaded confidence filtering for improved tracking-by-detection,” in Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part I 11.   Springer, 2010, pp. 369–382.
  17. A. Bewley, Z. Ge, L. Ott, F. T. Ramos, and B. Upcroft, “Simple online and realtime tracking,” in 2016 IEEE international conference on image processing (ICIP).   IEEE, 2016, pp. 3464–3468.
  18. R. E. Kalman et al., “Contributions to the theory of optimal control,” Bol. soc. mat. mexicana, vol. 5, no. 2, pp. 102–119, 1960.
  19. E. Bochinski, V. Eiselein, and T. Sikora, “High-speed tracking-by-detection without using image information,” in 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS).   IEEE, 2017, pp. 1–6.
  20. S. Blackman and R. Popoli, “Design and analysis of modern tracking systems(book),” Norwood, MA: Artech House, 1999., 1999.
  21. E. Bochinski, T. Senst, and T. Sikora, “Extending IOU based multi-object tracking by visual information,” in 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).   IEEE, 2018, pp. 1–6.
  22. S. Lyu, M.-C. Chang, D. Du, L. Wen, H. Qi, Y. Li, Y. Wei, L. Ke, T. Hu, M. Del Coco et al., “UA-DETRAC 2017: Report of avss2017 & iwt4s challenge on advanced traffic monitoring,” in Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on.   IEEE, 2017, pp. 1–7.
  23. L. Chen, H. Ai, Z. Zhuang, and C. Shang, “Real-time multiple people tracking with deeply learned candidate selection and person re-identification,” in 2018 IEEE international conference on multimedia and expo (ICME).   IEEE, 2018, pp. 1–6.
  24. Y. Wang, K. Kitani, and X. Weng, “Joint object detection and multi-object tracking with graph neural networks,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 13 708–13 715.
  25. C. Kim, F. Li, A. Ciptadi, and J. M. Rehg, “Multiple hypothesis tracking revisited,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 4696–4704.
  26. D. Musicki and R. Evans, “Joint integrated probabilistic data association: Jipda,” IEEE transactions on Aerospace and Electronic Systems, vol. 40, no. 3, pp. 1093–1099, 2004.
  27. Y. Bar-Shalom, T. Kirubarajan, and C. Gokberk, “Tracking with classification-aided multiframe data association,” IEEE Transactions on Aerospace and Electronic systems, vol. 41, no. 3, pp. 868–878, 2005.
  28. J. C. Meza, “Steepest descent,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 6, pp. 719–722, 2010.
  29. P. Dendorfer, H. Rezatofighi, A. Milan, J. Shi, D. Cremers, I. Reid, S. Roth, K. Schindler, and L. Leal-Taixé, “MOT20: A benchmark for multi object tracking in crowded scenes,” arXiv preprint arXiv:2003.09003, 2020.
  30. J. Luiten, A. Osep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, and B. Leibe, “HOTA: A higher order metric for evaluating multi-object tracking,” International journal of computer vision, vol. 129, pp. 548–578, 2021.
  31. E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi, “Performance measures and a data set for multi-target, multi-camera tracking,” in European conference on computer vision.   Springer, 2016, pp. 17–35.
  32. K. Bernardin and R. Stiefelhagen, “Evaluating multiple object tracking performance: the clear mot metrics,” EURASIP Journal on Image and Video Processing, vol. 2008, pp. 1–10, 2008.
  33. P. Dendorfer, A. Osep, A. Milan, K. Schindler, D. Cremers, I. Reid, S. Roth, and L. Leal-Taixé, “Motchallenge: A benchmark for single-camera multiple target tracking,” International Journal of Computer Vision, vol. 129, pp. 845–881, 2021.
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