Learning Scene Semantics

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. 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.