A Comprehensive Study of Object Tracking in Low-Light Environments
This addresses challenges in surveillance and ethology, but it is incremental as it builds on existing transformer-based trackers.
The paper tackled the problem of object tracking in low-light environments by integrating denoising and low-light enhancement methods into a transformer-based system, resulting in a tracker that outperformed MixFormer and Siam R-CNN.
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.