Spatio-temporal patches for night background modeling by subspace learning
In this paper, a novel background model on spatio-temporal patches is introduced for video surveillance, especially for night outdoor scene, where extreme lighting conditions often cause troubles. The spatio-temporal patch, called brick, is presented to simultaneously capture spatio-temporal information in surveillance video. The set of bricks of a given background patch, under all possible lighting conditions, lies in a low-dimensional subspace, which can be learned by online subspace learning. The proposed method can efficiently model the background and detect the appearance and motion variance caused by foreground. Experimental results on real data show that the proposed method is insensitive to dramatic lighting changes and achieves superior performance to two classical methods.