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Cascaded-Scoring Tracklet Matching for Multi-object Tracking

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14434))

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Abstract

Multi-object tracking (MOT) aims at locating the object of interest in a successive video sequence and associating the same moving object frame by frame. Most existing approaches to MOT lack the integration of both motion and appearance information, which limits the effectiveness of tracklet association. The conventional approaches for tracklet association often struggle when dealing with scenarios involving multiple objects with indistinguishable appearances and irregular motions, leading to suboptimal performance. In this paper, we introduce an appearance-assisted feature warper (AFW) module and a motion-guided based target aware (MTA) module to efficiently utilize the appearance and motion information. Additionally, we introduce a cascaded-scoring tracklet matching (CSTM) strategy that seamlessly integrates the two modules, combining appearance features with motion information. Our proposed online MOT tracker is called CSTMTrack. Through extensive quantitative and qualitative results, we demonstrate that our tracker achieves efficient and favorable performance compared to several other state-of-the-art trackers on the MOTChallenge benchmark.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant U21A20514, 62002302, by the FuXiaQuan National Independent Innovation Demonstration Zone Collaborative Innovation Platform Project under Grant 3502ZCQXT2022008, and by the China Fundamental Research Funds for the Central Universities under Grants 20720230038.

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Authors and Affiliations

  1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China

    Yixian Xie, Hanzi Wang & Yang Lu

Authors
  1. Yixian Xie
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  2. Hanzi Wang
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  3. Yang Lu
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Corresponding author

Correspondence to Yang Lu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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Xie, Y., Wang, H., Lu, Y. (2024). Cascaded-Scoring Tracklet Matching for Multi-object Tracking. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_14

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  • DOI: https://doi.org/10.1007/978-981-99-8549-4_14

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  • Online ISBN: 978-981-99-8549-4

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