A video-based abnormal human behavior detection for psychiatric patient monitoring

This paper proposes an abnormal human behavior detection system for monitoring psychiatric patient. A normal behavior can be characterized by the spatial and temporal features of human activities. The difficulty of abnormal behavior detection is that human behavior is unpredictable and complicated. It varies in both motion and appearance. The human behavior video stream is interspersed with transition of abnormal and normal events. Here, we propose an unsupervised learning using the N-cut algorithm along with the SVM to label the video segments and then apply the Condition random field (CRF) with an adaptive threshold to distinguish the normal and abnormal events.