A Low Illumination Environment Motion Detection Method based on Dictionary Learning

Huaxin Xiao, Yu Liu, Bin Wang, Shuren Tan, Maojun Zhang

Abstract: This paper proposes a dictionary-based motion detection method on video images captured under low light with serious noise. The proposed approach trains a dictionary by background images without foreground. It then reconstructs the test image according to the theory of sparse coding, and introduces the Structural Similarity Index Measurement (SSIM) as the detection standard to identify the detection caused by the brightness and contrast ratio changes. Experimental results show that compared to the mixture of Gaussian model and ViBe method, the proposed method can reach a better result under extreme low illumination circumstance.