Video-Based Detection of Abnormal Behavior in the Examination Room

Aiming at the problems of partial occlusion and background clutter in the examination room, we propose a method for behavior detection using spatial-temporal shape and flow correlation. The method first extracted training templates using interactive video cutout technique, and automatically segmented the video into 3D spatial-temporal volumes using improved Mean Shift algorithm. Then we slide the template across the video and compute the matching distance. We complement our shape-based features with flow, and efficiently match the volumetric representation of an action against over-segmented spatial-temporal video volumes. Thresholding the correlation distance and finding the peaks give us locations of potential matches. The experiment results indicate that this method achieves human´s action detection robustly in crowded, dynamic environment.