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

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

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

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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.

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Notes

  1. 1.

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

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Acknowledgement

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

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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
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  2. Huy Quang Che
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  3. Cuong Van Truong
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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

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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

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

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  • Print ISBN: 978-3-031-42429-8

  • Online ISBN: 978-3-031-42430-4

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