Intelligent Visual Surveillance: Towards Cognitive Vision Systems

Abstract

Automated visual surveillance systems are required to emulate the cognitive abilities of surveillance personnel, who are able to detect, recognise and assess the severity of suspicious, unusual and threatening behaviours. We describe the architecture of our surveillance system, emphasising some of its high-level cognitive capabilities. In particular, we present a methodology for automatically learning semantic labels of scene features and automatic detection of atypical events. We also describe a framework that supports learning of a wider range of semantics, using a motion attention mechanism and exploiting long-term consistencies in video data.