A Unified Transformer Based Tracker for Anti-UAV Tracking

Qianjin Yu, Yinchao Ma, Jianfeng He, Dawei Yang, Tianzhu Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 3036-3046


Recently, the need for advanced anti-UAV techniques is increasing due to the rising threat of unauthorized drone intrusion. Object tracking, specifically in thermal infrared (TIR) videos, offers a potential solution to this issue. However, the tracked target often suffers dramatic scale variation, frequent target disappearance, and camera movement which severely influence tracking performance. Therefore, we propose a Unified Transformer-based Tracker, dubbed UTTracker, which contains the following four modules. Firstly, a multi-region local tracking module is designed with temporal cues for tackling target appearance variation and multi-region search for tracking targets in multi proposals. Complementarily, a global detection module is introduced to meet the challenge of target frequent disappearance. Meanwhile, a background correction module is incorporated to align the backgrounds between adjacent frames for alleviating camera movement. Particularly, a dynamic small object detection module for tracking the small target that lacks appearance information. Thanks to the designed modules, our UTTracker can achieve robust UAV tracking in TIR scenarios. Numerous experiments on the 1st and 2nd anti-UAV benchmarks demonstrate the effectiveness of UTTracker. Notably, UTTracker is the foundation of the 2ndplace winning entry in the 3rd Anti-UAV Challenge.

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@InProceedings{Yu_2023_CVPR, author = {Yu, Qianjin and Ma, Yinchao and He, Jianfeng and Yang, Dawei and Zhang, Tianzhu}, title = {A Unified Transformer Based Tracker for Anti-UAV Tracking}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3036-3046} }