Modeling of temporarily static objects for robust abandoned object detection in urban surveillance
We propose a robust approach for abandoned object detection in urban surveillance with over thousands of cameras. For such a large-scale monitoring based on intelligent video analysis, it is critical that a system be designed with careful control of false alarms. Our approach is based on proactive modeling of temporally static objects (TSO) such as cars stopping at red light and still pedestrians in the street. We develop a finite state machine to track the entire life cycles of TSOs from creation to termination. The semantically meaningful object information provided by the state machine in turn allows adaptive region-level updating of the background model without using any sophisticated object classification techniques. We demonstrate that our approach significantly mitigates the problematic issue of false alarm related to people in city surveillance, using both a small publicly available data set and a large one collected from various realistic urban scenarios.