9. Hierarchical Behaviour Discovery

Behaviour of groups of objects observed in a crowded public space is typically complex and uncertain. What is considered to be ‘subjectively interesting behaviour’ to a human observer can be influenced by a wide variety of factors including: (1) the activity of a single object over time; (2) the correlated spatial states of multiple objects, for example, a piece of abandoned luggage is defined by separation from its owner; and (3) higher order spatial and temporal correlations among multiple entities, for instance, traffic flow at a road intersection has a particular spatio-temporal order beyond co-occurrence dictated by traffic lights. Constructing computational models that are both flexible and accurate in representing such complex and uncertain characteristics of behaviour is challenging. A dynamic topic model possesses unique computational attributes that make it an attractive framework for addressing these challenges. In this chapter, we describe a Markov clustering topic model designed for unsupervised modelling and on-line processing of multi-object spatio-temporal behaviours in crowded public scenes. A Markov clustering topic model draws on machine learning theories on probabilistic topic models and dynamic Bayesian networks to achieve a robust hierarchical modelling of behaviours and their dynamics.