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Nighttime Air Tracking Based on Improved Unsupervised Siamese Network

Published: 24 July 2024 Publication History

Abstract

The limited light at night reduces the contrast between the target and its surroundings, making it difficult for the tracking algorithms to distinguish the target from similar looking objects or background elements. As the UAV rotates, the speed of movement causes the appearance characteristics of the target to change slightly, and it is very difficult to label the dark night dataset at this stage. Therefore, we design an end-to-end tracking algorithm, TransffCAR, which first pre-processes the video frame images to increase their brightness and locate potential targets from a large number of unlabelled video frames to generate training patches, avoiding the need for tedious manual labelling. The feature-extracted video frames are fed into the Transformer connection layer, which is used to assist in capturing global contextual information from aerial imagery, and then into the Dynamic Template Tracking module for updating to adapt to changes in the target's appearance, followed by adaptive hierarchical feature fusion, which improves the ability to perceive the target's positional information, and the target's feature information is corrected to facilitate subsequent input into the area regression network. Generate clear predictive tracking maps. We use NAT2021, NAT2021L and UAVDark70 to test on public datasets and test three evaluation metrics that outperform other state-of-the-art tracking methods. The experimental results confirm the robustness of our designed method in handling the task of nighttime UAV aerial video tracking.

References

[1]
Cui, Y.; Jiang, C.; Wang, L.; Wu, G. Mixformer: End-to-end tracking with iterative mixed attention. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022; pp. 13608-13618.
[2]
Fu, Z.; Fu, Z.; Liu, Q.; Cai, W.; Wang, Y. SparseTT: Visual tracking with sparse transformers. arXiv preprint arXiv:2205.03776 2022.
[3]
Guo, D.; Wang, J.; Cui, Y.; Wang, Z.; Chen, S. SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In Proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020; pp. 6269-6277.
[4]
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019; pp. 4282-4291.
[5]
Ye, J.; Fu, C.; Zheng, G.; Paudel, D.P.; Chen, G. Unsupervised domain adaptation for nighttime aerial tracking. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022; pp. 8896-8905.
[6]
Yan, B.; Peng, H.; Fu, J.; Wang, D.; Lu, H. Learning spatio-temporal transformer for visual tracking. In Proceedings of the Proceedings of the IEEE/CVF international conference on computer vision, 2021; pp. 10448-10457.
[7]
Chen, F.; Wang, X.; Zhao, Y.; Lv, S.; Niu, X. Visual object tracking: A survey. Computer Vision and Image Understanding 2022, 222, 103508.
[8]
Ye, J.; Fu, C.; Zheng, G.; Cao, Z.; Li, B. Darklighter: Light up the darkness for uav tracking. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021; pp. 3079-3085.
[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 Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022; pp. 12146-12153.
[10]
Zhang, M.; Liu, J.; Wang, Y.; Piao, Y.; Yao, S.; Ji, W.; Li, J.; Lu, H.; Luo, Z. Dynamic context-sensitive filtering network for video salient object detection. In Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021; pp. 1553-1563.
[11]
Zheng, J.; Ma, C.; Peng, H.; Yang, X. Learning to track objects from unlabeled videos. In Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021; pp. 13546-13555.
[12]
Liu, Z.; Hu, H.; Lin, Y.; Yao, Z.; Xie, Z.; Wei, Y.; Ning, J.; Cao, Y.; Zhang, Z.; Dong, L. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022; pp. 12009-12019.
[13]
Liu, Y.; Shao, Z.; Hoffmann, N. Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561 2021.
[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 Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021; pp. 496-502.
[15]
Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 2014.
[16]
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019; pp. 4282-4291.
[17]
Wang, M.; Liu, Y.; Huang, Z. Large margin object tracking with circulant feature maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4021–4029.

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    CSAIDE '24: Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
    March 2024
    676 pages
    ISBN:9798400718212
    DOI:10.1145/3672919
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 24 July 2024

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