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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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References

  1. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional Siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56

    Chapter  Google Scholar 

  2. Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020)

    Article  Google Scholar 

  3. Cao, Z., Fu, C., Ye, J., Li, B., Li, Y.: HiFT: hierarchical feature transformer for aerial tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15457–15466 (2021)

    Google Scholar 

  4. Chen, M., et al.: Learning domain adaptive object detection with probabilistic teacher. In: International Conference on Machine Learning, pp. 3040–3055. PMLR (2022)

    Google Scholar 

  5. Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6668–6677 (2020)

    Google Scholar 

  6. Chen, Z., Zhu, L., Wan, L., Wang, S., Feng, W., Heng, P.A.: A multi-task mean teacher for semi-supervised shadow detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5611–5620 (2020)

    Google Scholar 

  7. Deng, J., Li, W., Chen, Y., Duan, L.: Unbiased mean teacher for cross-domain object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4091–4101 (2021)

    Google Scholar 

  8. Fan, H., et al.: LaSOT: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5374–5383 (2019)

    Google Scholar 

  9. Fu, C., Dong, H., Ye, J., Zheng, G., Li, S., Zhao, J.: HighlightNet: highlighting low-light potential features for real-time UAV tracking. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 12146–12153. IEEE (2022)

    Google Scholar 

  10. Guo, D., Wang, J., Cui, Y., Wang, Z., Chen, S.: SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6269–6277 (2020)

    Google Scholar 

  11. Hoyer, L., Dai, D., Van Gool, L.: DAFormer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9924–9935 (2022)

    Google Scholar 

  12. Huang, L., Zhao, X., Huang, K.: GOT-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1562–1577 (2019)

    Article  Google Scholar 

  13. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SiamRPN++: evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4282–4291 (2019)

    Google Scholar 

  14. Li, B., Fu, C., Ding, F., Ye, J., Lin, F.: ADTrack: target-aware dual filter learning for real-time anti-dark UAV tracking. In: 2021 IEEE International Conference on Robotics and Automation, pp. 496–502. IEEE (2021)

    Google Scholar 

  15. Li, C., Guo, C., Chen, C.: Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4225–38 (2021)

    Google Scholar 

  16. Liu, Y., Tian, Y., Chen, Y., Liu, F., Belagiannis, V., Carneiro, G.: Perturbed and strict mean teachers for semi-supervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4258–4267 (2022)

    Google Scholar 

  17. Lukezic, A., Matas, J., Kristan, M.: D3S-a discriminative single shot segmentation tracker. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7133–7142 (2020)

    Google Scholar 

  18. Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445–461. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_27

    Chapter  Google Scholar 

  19. Qiao, H., Zhong, S., Chen, Z., Wang, H.: Improving performance of robots using human-inspired approaches: a survey. Sci. China Inf. Sci. 65(12), 221201 (2022)

    Article  Google Scholar 

  20. Ramamonjison, R., Banitalebi-Dehkordi, A., Kang, X., Bai, X., Zhang, Y.: SimROD: a simple adaptation method for robust object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3570–3579 (2021)

    Google Scholar 

  21. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  22. Sun, Q., Zhao, C., Tang, Y., Qian, F.: A survey on unsupervised domain adaptation in computer vision tasks. Scientia Sinica (Technologica) 52(1), 26–54 (2022)

    Article  Google Scholar 

  23. Tang, S., Andriluka, M., Andres, B., Schiele, B.: Multiple people tracking by lifted multicut and person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3539–3548 (2017)

    Google Scholar 

  24. Tang, Y., et al.: Perception and navigation in autonomous systems in the era of learning: a survey. IEEE Trans. Neural Netw. Learn. Syst. (2022)

    Google Scholar 

  25. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  26. Xu, Y., Wang, Z., Li, Z., Yuan, Y., Yu, G.: SiamFC++: towards robust and accurate visual tracking with target estimation guidelines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12549–12556 (2020)

    Google Scholar 

  27. Ye, J., Fu, C., Cao, Z., An, S., Zheng, G., Li, B.: Tracker meets night: a transformer enhancer for UAV tracking. IEEE Robot. Autom. Lett. 7(2), 3866–3873 (2022)

    Article  Google Scholar 

  28. Ye, J., Fu, C., Zheng, G., Cao, Z., Li, B.: DarkLighter: light up the darkness for UAV tracking. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3079–3085. IEEE (2021)

    Google Scholar 

  29. Ye, J., Fu, C., Zheng, G., Paudel, D.P., Chen, G.: Unsupervised domain adaptation for nighttime aerial tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8896–8905 (2022)

    Google Scholar 

  30. Zhang, L., Gonzalez-Garcia, A., Weijer, J.V.D., Danelljan, M., Khan, F.S.: Learning the model update for Siamese trackers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4010–4019 (2019)

    Google Scholar 

  31. Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W.: Ocean: object-aware anchor-free tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 771–787. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58589-1_46

    Chapter  Google Scholar 

  32. Zhao, J.X., Liu, J.J., Fan, D.P., Cao, Y., Yang, J., Cheng, M.M.: EGNet: edge guidance network for salient object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8779–8788 (2019)

    Google Scholar 

  33. Zhou, H., Jiang, F., Lu, H.: SSDA-YOLO: semi-supervised domain adaptive yolo for cross-domain object detection. arXiv preprint: arXiv:2211.02213 (2022)

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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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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8180-9

  • Online ISBN: 978-981-99-8181-6

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