Video activity detection using compressed domain motion trajectories for H.264 videos

Surveillance videos are often compressed for transmission or storage. It is desirable to be able to perform automatic event detection in the compressed domain directly. In this paper, we investigate the use of motion trajectories for video activity detection in the compressed domain. We show that it is possible to extract reliable motion trajectories directly from compressed H.264 video streams. To overcome the problems caused by unreliable motion vectors, we propose to include the information from the compressed domain prediction residuals to make the tracking more robust. We also show a real world application based on the classification of the motion trajectories to detect vacant or occupied parking spaces.