A completely autonomous system that learns anomalous movements in advanced videosurveillance applications

This paper describes an automatic real-time video surveillance system, capable of autonomously learning and signaling anomalous activities of moving objects. To obtain these capabilities, an improved version of the altruistic vector quantization algorithm (AVQ) is proposed. The modified AVQ automatically evaluates the number of trajectory prototypes, and improves the representativeness of the prototypes themselves, so the visual events can be easily and accurately classified. Anomalous behaviors are detected if visual trajectories deviate from the self-learned representations of "typical" behaviors. The system has been implemented by means of standard PCs and TV cameras, and has been tested in many real outdoor contexts in different conditions (night and day). Currently it is used to monitor the storage areas of British Airways at the airport of Peretola (Florence, Italy), and some access gates of Autostrade per FItalia S.p.A. (the main Italian highways company). If the camera field-of-view is changed, the system automatically re-learns new "typical" behaviors and accurately detects anomalous events.