Motion segmentation and tracking using normalized cuts

We propose a motion segmentation algorithm that aims to break a scene into its most prominent moving groups. A weighted graph is constructed on the image sequence by connecting pixels that are in the spatiotemporal neighborhood of each other. At each pixel, we define motion profile vectors which capture the probability distribution of the image velocity. The distance between motion profiles is used to assign a weight on the graph edges. Using normalised cuts we find the most salient partitions of the spatiotemporal graph formed by the image sequence. For segmenting long image sequences, we have developed a recursive update procedure that incorporates knowledge of segmentation in previous frames for efficiently finding the group correspondence in the new frame.