Real-time video-shot detection for scene surveillance applications.

In this paper, a surveillance system with automatic video-shot detection and indexing capabilities is presented. The proposed system aims at detecting the presence of abandoned objects in a guarded environment and at automatically performing online semantic video segmentation in order to facilitate the human operator's task of retrieving the cause of an alarm. The former task is performed by operating image segmentation based on temporal rank-order filtering, followed by classification in order to reduce false alarms. The latter task is performed by operating temporal video segmentation when an alarm is detected. In the clips of interest, the key frame is the one depicting a person leaving a dangerous object, and is determined on the basis of a feature indicating the movement around the dangerous region. Experimental results are reported in terms of static region detection, classification, clip and key-frame detection errors versus different levels of complexity of the guarded environment, in order to establish the performance that can be expected from the system in different situations.