Prior to the correlation filter target tracking algorithm, the research work on low-illumination widefield tiny target tracking has not been reported. In response to this, this paper proposes an algorithm that combines an image difference detection framework with a correlation filter (CF)-based tracking framework, and introduces dual filters to combat the adverse factors brought by the environment. A sparse response regularization term of the l2 norm is proposed to suppress the abnormal peaks produced by the CF framework. In the response phase, the positions of small objects are predicted based on the dual filter weighted fusion. The results show that the proposed algorithm has excellent tracking performance for fast moving, deformation and motion blur of small targets in low illumination and wide field of view, and meets real-time performance. A new dataset of 41 night surveillance sequences captured by Eagle Eye cameras was collected as a benchmark. The experimental results show that the algorithm in this paper improves DP by 8. 8%, AUC by 7. 4%, and realizes real-time operation of 30. 6 frames per second on a single CPU.