Sebastian Lühr, Svetha Venkatesh, Geoff W. West and Hung H. Bui. (2004) Explicit state duration HMM for abnormality detection in sequences of human activity. In Proc. 8th Pacific Rim Intl Conf. Artificial Intelligence, volume 3157 of Lecture Notes in Artificial Intelligence, pages 983–984. Springer-Verlag, August 2004.

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Introduction: Much of the current work in human behaviour modelling concentrates on activity recognition, recognising actions and events through pose, movement, and gesture analysis. Our work focuses on learning and detecting abnormality in higher level behavioural patterns. The hidden Markov model (HMM) is one approach for learning such behaviours given a vision tracker recording observations about a person’s activity. We show how the implicit state duration in the HMM can create a situation in which highly abnormal deviation as either less than or more than the usually observed activity duration can fail to be detected and how the explicit state duration HMM (ESD-HMM) helps alleviate the problem. Duration of human activity is an important consideration if we are to accurately model a person s behavioural patterns.