A fast approach of object segmentation for video sequence

In this paper, we present a novel video object segmentation approach. The proposed approach extracts objects from a frame in a video stream using the difference information between the mean-removed versions of the current and referenced frames. Due to the mean-removed version of a frame reduces the influence of light variation on the frame and reserves the texture information of the frame, the proposed approach can effectively segment objects for video sequences and remove shadow pixels. Experimental results show that the proposed approach has the least computation time among object segmentation approaches with shadow removal capability. Compared with the available approaches, our approach reduces the computation time by 7% to 58% with better segmentation accuracy.