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.

在低光环境中进行准确的物体追踪对于监控和动物行为学应用至关重要。本文研究了这些扭曲对自动物体追踪器的影响，并提出了一种解决方案，通过将去噪和低光增强方法整合到基于变压器的物体追踪系统中，提高追踪性能。实验结果表明，用低光合成数据集训练的提出的追踪器优于原始的 MixFormer 和 Siam R-CNN。