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Multi-object Tracking Based on Nearest Optimal Template Library

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Artificial Neural Networks and Machine Learning – ICANN 2021 (ICANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12891))

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Abstract

Noisy detection and similar appearance lead to deteriorated mis-identification and id-switch in Multi-Object Tracking (MOT). To address these problems, we propose a novel Nearest Optimal Template Library (NOTL) associated with two tailor-made methods based on the NOTL. Here, the NOTL is a historical sample set of the tracked objects, and the elements in the NOTL are closest to the complete object at the current instant. It provides reliable appearance information of the object. Then, we use the single object tracker (SOT) for position prediction, and spatio-temporal network for appearance modeling. They can alleviate mis-identification and id-switch problems, respectively. Besides, the triplet loss is used to train our spatio-temporal network further improves the performance. The proposed algorithm achieves 55.3% and 55.1% in MOTA on challenging MOT16 and MOT17 benchmark datasets respectively. These results show our method is competitive with the previous state-of-the-art approaches.

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Acknowledgements

This work was supported by the Project of Quzhou Municipal Government (2020D011), and National Science Foundation of China (U19A2052).

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Authors and Affiliations

  1. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, Zhejiang, China

    Ran Tian, Xiang Zhang, Donghang Chen & Yujie Hu

Authors
  1. Ran Tian
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  2. Xiang Zhang
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  3. Donghang Chen
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  4. Yujie Hu
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Corresponding author

Correspondence to Xiang Zhang .

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Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Tian, R., Zhang, X., Chen, D., Hu, Y. (2021). Multi-object Tracking Based on Nearest Optimal Template Library. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12891. Springer, Cham. https://doi.org/10.1007/978-3-030-86362-3_27

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  • DOI: https://doi.org/10.1007/978-3-030-86362-3_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86361-6

  • Online ISBN: 978-3-030-86362-3

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