Automated human recognition and tracking for video surveillance system

Abstract

Recent research in video surveillance system has shown an increasing focus on creating reliable systems utilizing non-computationally expensive technique for observing humans' appearance, movements and activities, thus providing analytical information for advanced human behavior analysis and realistic human modeling. In order for the system to function, it requires robust method for detecting and tracking human from a given input of video streams. In this thesis, a human detection technique suitable for video surveillance is presented which requires fast computations in addition of accurate results. The techniques proposed include adaptive frame differencing for background subtraction, contrast adjustment for shadow removal, and shape based approach for human detection. The tracking technique on the other hand uses correspondence approach. Event Based Video Retrieval (EBVR) system is also proposed for efficient surveillance data management and automated human recognition with unique ID assignment. Proposed human detection and tracking are integrated with EBVR and motion detection into a complete automated surveillance system called Active Vis Video Surveillance Analysis System (AVSAS) which produces good result and real-time performance especially in non-crowded scene. The EBVR system also proves to be able to handle automated human recognition with unique ID assignment accurately.