Journal of Applied Science and Engineering

Published by Tamkang University Press

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A Visual Tracking Method for Low-Light-Induced Response Degradation

 2026

 Volume 35

Jinyu Ren, Jiafei Xie, Qi Yang, and Chonglei Shao

School of Mechanical Engineering, Shenyang Ligong University, Shenyang, Liaoning, China

Received: August 18, 2026
Accepted: September 11, 2026
Publication Date: September 25, 2026

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Overall framework of the proposed method.

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Robust visual object tracking is a fundamental perception capability that enables mobile robots to follow targets continuously in low-light environments. Insufficient illumination degrades informative cues such as target texture, contours, and local appearance details, resulting in ambiguous response peaks and competition among multiple candidate hypotheses. Under severe visual degradation, the highest-response location may no longer correspond to the true target. To address this issue, we propose a reliability-aware candidate selection framework specifically designed for low-light object tracking. Rather than relying solely on the global response maximum, the proposed framework retains multiple candidate hypotheses corresponding to spatially distinct response peaks. A candidate reliability assessment module then integrates the current visual representation of each candidate, the fused features generated by the base tracker, the historical target representation, and the geometric relations and response differences among candidates to jointly estimate their relative localization quality and replacement confidence. The original prediction is revised only when an alternative candidate demonstrates both a sufficient localization-quality advantage and sufficient replacement confidence, thereby reducing the risk of erroneous candidate switching and tracking drift. Experiments on the LLOT dataset and low-light videos captured by a real robot demonstrate that the proposed method alleviates response ambiguity and reduces candidate misselection, leading to more accurate target localization and more stable tracking.

Keywords: Low-light object tracking; Response degradation; Reliability-aware candidate selection; Relative localization quality; Replacement confidence

  1. [1] P. Zhong, X. Guo, D. Huang, et al., (2026) “Low-Light Object Tracking: A Benchmark” IEEE Transactions on Intelligent Vehicles 11(1): 220-235. DOI: https://doi.org/10.1109/TIV.2025.3621205.
  2. [2] J. Zhu, H. Tang, Z. Q. Cheng, et al. “DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs”. In: 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, 7381-7388. DOI: https://doi.org/10.1109/ICRA57147.2024.10610544.
  3. [3] W. Cai, Q. Liu, and Y. Wang. “HIPTrack: Visual Tracking with Historical Prompts”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2024, 19258-19267. DOI: https://doi.org/10.1109/CVPR52733.2024.01822.
  4. [4] J. P. Lan, Z. Q. Cheng, J. Y. He, et al. “ProContEXT: Exploring Progressive Context Transformer for Tracking”. In: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, 1-5. DOI: https://doi.org/10.1109/ICASSP49357.2023.10094971.
  5. [5] X. Wei, Y. Bai, Y. Zheng, et al. “Autoregressive Visual Tracking”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023, 9697-9706. DOI: https://doi.org/10.1109/CVPR52729.2023.00935.
  6. [6] Q. Wu, T. Yang, Z. Liu, et al. “DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023, 14561-14571. DOI: https://doi.org/10.1109/CVPR52729.2023.01399.
  7. [7] J. Xie, B. Zhong, Z. Mo, et al. “Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2024, 19300-19309. DOI: https://doi.org/10.1109/CVPR52733.2024.01826.
  8. [8] Y. Cai, J. Liu, J. Tang, et al. “Robust Object Modeling for Visual Tracking”. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, 2023, 9555-9566. DOI: https://doi.org/10.1109/ICCV51070.2023.00879.
  9. [9] B. Ye, H. Chang, B. Ma, et al. “Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework”. In: European Conference on Computer Vision. Springer, 2022, 341-357. DOI: https://doi.org/10.1007/978-3-031-20047-2_20.
  10. [10] S. Gao, C. Zhou, and J. Zhang. “Generalized Relation Modeling for Transformer Tracking”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023, 18686-18695. DOI: https://doi.org/10.1109/CVPR52729.2023.01792.
  11. [11] B. Yan, H. Peng, J. Fu, et al. “Learning Spatio-Temporal Transformer for Visual Tracking”. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, 2021, 10428-10437. DOI: https://doi.org/10.1109/ICCV48922.2021.01028.
  12. [12] X. Chen, H. Peng, D. Wang, et al. “SeqTrack: Sequence to Sequence Learning for Visual Object Tracking”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023, 14572-14581. DOI: https://doi.org/10.1109/CVPR52729.2023.01400.
  13. [13] Y. Kou, J. Gao, B. Li, et al. “ZoomTrack: Target-aware Non-Uniform Resizing for Efficient Visual Tracking”. In: Advances in Neural Information Processing Systems. 36. Curran Associates, Inc., 2023, 50959-50977. DOI: https://doi.org/10.52202/075280-2217.
  14. [14] B. Chen, P. Li, L. Bai, et al. “Backbone Is All Your Need: A Simplified Architecture for Visual Object Tracking”. In: European Conference on Computer Vision. Springer, 2022, 375-392. DOI: https://doi.org/10.1007/978-3-031-20047-2_22.
  15. [15] F. Li, C. Tian, W. Zuo, et al. “Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018, 4904-4913. DOI: https://doi.org/10.1109/CVPR.2018.00515.
  16. [16] M. Danelljan, G. Hager, F. S. Khan, et al. “Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016, 1430-1438. DOI: https://doi.org/10.1109/CVPR.2016.159.
  17. [17] L. Bertinetto, J. Valmadre, S. Golodetz, O. Miksik, and P. H. S. Torr. “Staple: Complementary Learners for Real-Time Tracking”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016, 1401-1409. DOI: https://doi.org/10.1109/CVPR.2016.156.
  18. [18] J. Ye, C. Fu, G. Zheng, et al. “Unsupervised Domain Adaptation for Nighttime Aerial Tracking”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2022, 8886-8895. DOI: https://doi.org/10.1109/CVPR52688.2022.00869.
  19. [19] C. Fu, L. Yao, H. Zuo, et al. “SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation”. In: 2024 International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, 2024, 31-38. DOI: https://doi.org/10.1109/ICARM62033.2024.1071590.
  20. [20] B. Li, C. Fu, F. Ding, et al., (2023) “All-Day Object Tracking for Unmanned Aerial Vehicle” IEEE Transactions on Mobile Computing 22(8): 4515-4529. DOI: https://doi.org/10.1109/TMC.2022.3162892.
  21. [21] C. Mayer, M. Danelljan, D. P. Paudel, and L. Van Gool. “Learning Target Candidate Association to Keep Track of What Not to Track”. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE, 2021, 13424-13434. DOI: https://doi.org/10.1109/ICCV48922.2021.01319.
  22. [22] M. Danelljan, L. Van Gool, and R. Timofte. “Probabilistic Regression for Visual Tracking”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2020, 7181-7190. DOI: https://doi.org/10.1109/CVPR42600.2020.00721.