Image Processing System for Pedestrian Monitoring using neural classification of normal motion patterns

Automated surveillance of crowded dynamic scenes requires prompt detection and classification of unusual activities as means of alerting operators to potentially dangerous situations as they arise. Motion is a strong cue that can be used to classify dynamic scenes and hence detect abnormal movements that can be related to critical situations. Here we propose a method to detect such unusual movements by learning the normal motion characteristics of a dynamic scene and using the acquired knowledge to detect the unusual cases. We consider a typical CCTV scenario in Liverpool Street Underground Station in the City of London as an application example and we evaluate the system performance on more than 1800 events to demonstrate its practicality and reliability.