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.

翻译：低光环境下的精确目标跟踪至关重要，尤其是在监控和动物行为学应用中。然而，由于捕获序列质量低下，实现这一目标面临显著挑战。噪声、色彩失衡和低对比度等因素加剧了这些困难。本文开展了一项综合研究，系统考察这些失真对自动目标跟踪器的影响。此外，我们提出了一种解决方案，通过将去噪和低光增强方法集成到基于Transformer的目标跟踪系统中，提升跟踪性能。实验结果表明，采用低光合成数据集训练的所提出的跟踪器，性能优于原始的MixFormer和Siam R-CNN。