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Rethinking Unsupervised Domain Adaptation for Nighttime Tracking

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1968))

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Abstract

Despite the considerable progress that has been achieved in visual object tracking, it remains a challenge to track in low-light circumstances. Prior nighttime tracking methods suffer from either weak collaboration of cascade structures or the lack of pseudo supervision, and thus fail to bring out satisfactory results. In this paper, we develop a novel unsupervised domain adaptation framework for nighttime tracking. Specifically, we benefit from the establishment of pseudo supervision in the mean teacher network, and further extend it with three components at the input level and the optimization level. For the unlabeled target domain dataset, we first present an assignment-based object discovery strategy to generate suitable training patches. Additionally, a low-light enhancer is embedded to improve the pseudo labels that facilitate the following consistency learning. Finally, with the aid of better training data and pseudo labels, we replace the common mean square error with two stricter losses, which are entropy-decreasing classification consistency loss and confidence-weighted regression consistency loss, for better convergence. Experiments demonstrate that our proposed method achieves significant performance gains on multiple nighttime tracking benchmarks, and even brings slight enhancement on the source domain.

Supported by National Natural Science Foundation of China (62233005, 62293502), Program of Shanghai Academic Research Leader Under Grant 20XD1401300, Sino-German Center for Research Promotion (Grant M-0066) and Fundamental Research Funds for the Central Universities(222202317006)

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Jiaying Chen, Qiyu Sun, Chaoqiang Zhao, Wenqi Ren & Yang Tang

Authors
  1. Jiaying Chen
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  2. Qiyu Sun
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  3. Chaoqiang Zhao
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  4. Wenqi Ren
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  5. Yang Tang
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Corresponding author

Correspondence to Yang Tang .

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Editors and Affiliations

  1. Scholl of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Chen, J., Sun, Q., Zhao, C., Ren, W., Tang, Y. (2024). Rethinking Unsupervised Domain Adaptation for Nighttime Tracking. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_30

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  • DOI: https://doi.org/10.1007/978-981-99-8181-6_30

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  • Online ISBN: 978-981-99-8181-6

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