Learning Behavioural Context

Abstract

Visual context is the environment, background, and settings within which objects and associated behaviours are observed visually. A semantic interpretation of behaviour depends largely on knowledge of spatial and temporal context defining where and when it occurs, and correlational context specifying the expectation on the behaviours of correlated other objects in its vicinity. In this chapter, we address the problem of how to model computationally behavioural context for context-aware behavioural anomaly detection in a visual scene. We consider models for learning three types of behavioural context: spatial context, correlational context and temporal context. Behaviour spatial context provides situational awareness about where a behaviour is likely to take place. A public space can often be segmented by activities into a number of distinctive regions, called “semantic regions”. Behaviours of certain characteristics are expected in one region but differ from those observed in other regions. Behaviour correlational context specifies how the interpretation of a behaviour can be affected by behaviours of other objects either nearby in the same semantic region or further away in other regions. Object behaviours in a complex scene are often correlated and need be interpreted together rather than in isolation. Behaviour temporal context provides information regarding when different behaviours are expected to happen both inside each semantic region and across regions.