Automated Visual Surveillance Using Hidden Markov Models

This paper describes an automated visual surveillance system that detects suspicious human activity in a scene. The system is designed to: 1) detect and track people in the scene, 2) recognize the “normal” activities in the scene, and 3) detect anomalous activity by finding sufficiently large deviations from the normal activity patterns. The stochastic time-sequence recognition framework of the Hidden Markov Model (HMM) forms the basis of activity recognition and anomaly detection. We have implemented the system to monitor an office corridor in real-time using a Pentium III machine running Windows 2000. The results show that the system correctly classifies examples of normal activities in the corridor and identifies a mock break-in attempt as suspicious activity.