Human action recognition in crowded surveillance video sequences by using features taken from key-point trajectories

There is a need for systems that can automatically detect specific human actions in a surveillance video. However, almost all of the human action recognition techniques proposed so far are for detecting relatively large actions within simple video sequences. To alleviate this shortcoming, we propose a method that can detect specific actions within crowd sequences of real surveillance video. Our action recognition method is based on the bag-of-features approach, and key-point trajectories are used as its features. One problem is that key-point trajectories cannot be directly input when using the bag-of-features approach, because they have various time lengths. To overcome this difficulty, our method extracts a fixed-length feature descriptor from a key-point trajectory and uses it for event classification. In addition, feature weights are calculated for reducing the interference from noise trajectories in the background regions. Our method could more precisely detect specific actions than conventional other methods, and it performed well in the TRECVID 2010 Surveillance Event Detection task.