  Accurate object tracking in low-light environments is crucial, particularly
in surveillance and ethology applications. However, achieving this is
significantly challenging due to the poor quality of captured sequences.
Factors such as noise, color imbalance, and low contrast contribute to these
challenges. This paper presents a comprehensive study examining the impact of
these distortions on automatic object trackers. Additionally, we propose a
solution to enhance tracking performance by integrating denoising and low-light
enhancement methods into the transformer-based object tracking system.
Experimental results show that the proposed tracker, trained with low-light
synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN.

Computer Vision and Pattern Recognition