Low-Light Video Object Tracking with Data Augmentation and Feature Alignment
Yihao Qian, Daxing Zhang · 2024
Recent deep learning-based video object tracking approaches have shown promising performance on standard tracking benchmarks. However, the scarcity of low-light scenes in common tracking datasets poses a significant challenge for developing robust low-light deep trackers, especially for the Unmanned Aerial Vehicle (UAV) tracking scenario with more difficulty for data collection, thus severely degrading the performance of existing deep trackers in low-light UAV conditions. In this paper, we propose a simple yet effective approach to enhance UAV tracking in low-light conditions. Different from previous works that need annotated pairwise images for lightness enhancement, our proposed approach is annotation free, which can be generally used to improve existing trackers in low-light scenario. Specifically, we first simulate low-light conditions by performing simple data augmentation on normal tracking videos. We then conduct the feature alignment between the normal and low-light video frames, in order to facilitate the trackers to adapt well in low-light conditions. Surprisingly, we find that our simple approach can effectively improve existing deep trackers, including both typical CNN-based SiamFC and transformer-based OSTrack. Experiments on UAVDark135 show that our proposed trackers can achieve favorable performance with simple design, which has the potential to be served as a simple yet effective baseline in low-light UAV tracking.