Research on the perception method of tiny objects in low-light and wide-field video.

At present, many trackers exhibit commendable performance in well-illuminated scenarios but overlook target tracking in low-light environments. As night falls, the tracker's accuracy drops dramatically. Challenges such as high image resolution, intricate backgrounds, uneven illumination, and the resemblance between targets and backgrounds in Hawk-Eye surveillance videos make tracking small objects in low-light and wide-field scenarios exceedingly difficult for previous trackers. To address these challenges, this paper introduces an innovative approach by integrating the difference constraint method into the CF (correlation filters) tracker, which generates a change-aware mask using inter-frame difference information. In addition, a dual regression model and inter-frame difference constraint term are introduced to restrict each other for dual filter learning. In this paper, we construct a new benchmark comprising 41 night surveillance sequences captured by Hawk-Eye cameras. Exhaustive experiments are conducted on this benchmark. The results show that the proposed method maintains superior accuracy, surpasses state-of-the-art trackers in this dataset, and achieves a real-time performance of 27 fps on a single CPU, substantially advancing tiny object tracking on Hawk-Eye surveillance videos in low light and in night scenes.