Clustering Point Trajectories with Various Life-Spans
Motion-based segmentation of a sequence of images is an essential step for many applications of video analysis, including action recognition and surveillance. This paper introduces a new approach to motion segmentation operating on point trajectories. Each of these trajectories has its own start and end instants, hence its own life-span, depending on the pose and appearance changes of the object it belongs to. A set of such trajectories is obtained by tracking sparse interest points. Based on an adaptation of recently proposed J-linkage method, these trajectories are then clustered using series of affine motion models estimated between consecutive instants, and an appropriate residual that can handle trajectories with various life-spans. Our approach does not require any completion of trajectories whose life-span is shorter than the sequence of interest. We evaluate the performance of the single cue of motion, without considering spatial prior and appearance. Using a standard test set, we validate our new algorithm and compare it to existing ones. Experimental results on a variety of challenging real sequences demonstrate the potential of our approach.