Abstract
Background subtraction is a common method utilized to detect moving objects. The main idea is estimate the background model according to the non-occluded background. However, when the foreground is comparatively large or the moving displacement of foreground is negligible, the estimated result will be inaccurate because the background is occluded by foreground most of the time. In order to overcome the occluded background problem, we consider the spatial low-rank property of background, and propose to combine the spatial low-rank property and the temporal low-rank property to better characterize the strong correlation existing in spatio-temporal dimension of the background. The proposed method extends the low-rank matrix modeling to low-rank tensor modeling for the background. Experimental results show that the low-rank tensor modeling improves the result under occluded background or highly structured background.