ECAI-2002 Conference Paper

[full paper]

Modeling Interaction Using Learnt Qualitative Spatio-Temporal Relations and Variable Length Markov Models

Aphrodite Galata, Anthony G. Cohn, Derek Magee, David Hogg

Motivated by applications such as automated visual surveillance and video monitoring and annotation, there has been a lot of interest in constructing cognitive vision systems capable of interpreting the high level semantics of dynamic scenes. In this paper we present a novel approach for automatically inferring models of object interactions that can be used to interpret observed behaviour within a scene. A real-time low-level computer vision system, together with an attentional control mechanism, are used to identify incidents or events that occur in the scene. A data driven approach has been taken in order to automatically infer discrete and abstract representations (symbols) of primitive object interactions;effectively the system learns a set of qualitative spatial relations relevant to the dynamic behaviour of the domain. These symbols then form the alphabet of a VLMM which automatically infers the high level structure of typical interactive behaviour. The learnt behaviour model has generative capabilities and is also capable of recognizing typical or atypical activities within a scene. Experiments have been performed within the traffic monitoring domain; however the proposed method is applicable to the general automatic surveillance task since it does not assume a priori knowledge of a specific domain.

Keywords: Vision, Learning, Machine Learning

Citation: Aphrodite Galata, Anthony G. Cohn, Derek Magee, David Hogg: Modeling Interaction Using Learnt Qualitative Spatio-Temporal Relations and Variable Length Markov Models . In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.741-745.