Skip to main content
SpringerLink
Log in

Fast Camera Motion Compensation Based Kalman Filter and Cascade Association for Multi-object Tracking

  • Conference paper
  • First Online:
Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1863))

Included in the following conference series:

  • 316 Accesses

Abstract

Numerous ways to improve the power of tracking algorithms have emerged in modern multi-object tracking problems. Tracking-by-detection, On the other hand, is one of the most precise approaches in the field, balancing the trade-off between precision and run-time. This method divides the tracking process into two steps: the detection process to localize objects in the image and the tracking process to assign identity for each response from the object detector. In this study, we optimize the tracking process by generalizing the BYTE technique (Cascade Association) and integrating camera-motion compensation to the Association stage. Our new tracker KCM-Track, sets a new state-of-the-art accuracy on MOT17 dataset in terms of the primary MOT metrics: MOTA, IDF1, and HOTA. On MOT17 test sets: \(\mathbf{80.6}\%\) MOTA, \(\mathbf{79.7}\%\) IDF1, and \(\mathbf{64.6}\%\) HOTA are achieved at \(\textbf{314}\) FPS for the tracking process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/JonathonLuiten/TrackEval.

References

  1. Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1), 83–97 (1955). https://doi.org/10.1002/NAV.3800020109

    Article  MathSciNet  MATH  Google Scholar 

  2. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960). https://doi.org/10.1115/1.3662552

    Article  MathSciNet  Google Scholar 

  3. Aharon, N., Orfaig, R., Bobrovsky, B.Z.: Bot-sort: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022)

  4. Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008 (2008). https://doi.org/10.1155/2008/246309

  5. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468 (2016). https://doi.org/10.1109/ICIP.2016.7533003

  6. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–123 (2000)

    Google Scholar 

  7. Chu, P., Wang, J., You, Q., Ling, H., Liu, Z.: Transmot: spatial-temporal graph transformer for multiple object tracking. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4859–4869 (2023). https://doi.org/10.1109/WACV56688.2023.00485

  8. Du, Y., Song, Y., Yang, B., Zhao, Y.: Strongsort: Make deepsort great again. arXiv preprint arXiv:2202.13514 (2022)

  9. Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)

  10. Liang, C., Zhang, Z., Zhou, X., Li, B., Zhu, S., Hu, W.: Rethinking the competition between detection and reid in multiobject tracking. IEEE Trans. Image Process. 31, 3182–3196 (2022). https://doi.org/10.1109/TIP.2022.3165376

    Article  Google Scholar 

  11. Luiten, J., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vis. , 1–31 (2020). https://doi.org/10.1007/s11263-020-01375-2

  12. Milan, A., Leal-Taixe, L., Reid, I., Roth, S., Schindler, K.: Mot16: A benchmark for multi-object tracking (2016). https://doi.org/10.48550/ARXIV.1603.00831, arxiv.org/abs/1603.00831

  13. Pang, J., et al.: Quasi-dense similarity learning for multiple object tracking. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  14. Peng, J., et al.: Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. In: Proceedings of the European Conference on Computer Vision (2020)

    Google Scholar 

  15. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) Computer Vision - ECCV 2016 Workshops. LNCS, pp. 17–35. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2

    Chapter  Google Scholar 

  16. Sun, P., et al.: Transtrack: Multiple-object tracking with transformer. arXiv preprint arXiv: 2012.15460 (2020)

  17. Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 107–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_7

    Chapter  Google Scholar 

  18. Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649. IEEE (2017). https://doi.org/10.1109/ICIP.2017.8296962

  19. Wu, J., Cao, J., Song, L., Wang, Y., Yang, M., Yuan, J.: Track to detect and segment: an online multi-object tracker. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  20. Yang, F., Odashima, S., Masui, S., Jiang, S.: Hard to track objects with irregular motions and similar appearances? make it easier by buffering the matching space (2022). https://doi.org/10.48550/ARXIV.2211.14317, arxiv.org/abs/2211.14317

  21. Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., Wei, Y.: Motr: end-to-end multiple-object tracking with transformer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. LNCS, pp. 659–675. Springer Nature Switzerland, Cham (2022). https://doi.org/10.1007/978-3-031-19812-0_38

    Chapter  Google Scholar 

  22. Zhang, Y., et al.: Bytetrack: Multi-object tracking by associating every detection box (2022)

    Google Scholar 

  23. Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: Fairmot: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vis. 129, 3069–3087 (2021)

    Article  Google Scholar 

  24. Zhou, X., Koltun, V., Krähenbühl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. LNCS, pp. 474–490. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_28

    Chapter  Google Scholar 

  25. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. In: arXiv preprint arXiv:1904.07850 (2019)

Download references

Acknowledgement

This research is funded by University of Information Technology-Vietnam National University of Ho Chi Minh city under grant number D1-2023-14.

Author information

Authors and Affiliations

  1. University of Information Technolog, Ho Chi Minh City, Vietnam

    Tung Thanh Do, Huy Quang Che & Cuong Van Truong

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Tung Thanh Do, Huy Quang Che & Cuong Van Truong

Authors
  1. Tung Thanh Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huy Quang Che
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cuong Van Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tung Thanh Do .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut's Institute of Technology, Ladkrabang, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Do, T.T., Che, H.Q., Truong, C.V. (2023). Fast Camera Motion Compensation Based Kalman Filter and Cascade Association for Multi-object Tracking. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42430-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42429-8

  • Online ISBN: 978-3-031-42430-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation