Coherent event-based surveillance video synopsis using trajectory clustering

With the rapid development of the camera industry, surveillance systems become more and more popular in our daily life. However, it is very time-consuming to find out specific persons or objects from a mass of surveillance videos with long duration. For efficient browsing surveillance videos, numerous researchers are devoted to eliminating the inherent spatiotemporal redundancy for video synopsis. Nevertheless, too much information in a synopsis frame may distract viewers' attention. Therefore, we propose a novel surveillance video synopsis system using coherent event classification to alleviate the above issues. Object trajectories are extracted by background subtraction, and then clustered. Coherent events containing similar actions of objects with different moving speeds are obtained by applying the longest common subsequence algorithm to measure the similarity among trajectories. The trajectories in each cluster are rescheduled and stitched onto the background to generate synopsis videos with coherent events. Comprehensive experiments conducted on various surveillance videos demonstrate the convincing performance of our proposed system.