Explicit State Duration HMM for Abnormality Detection in Sequences of Human Activity

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. Duration of human activity is an important consideration if we are to accurately model a person’s behavioural patterns. 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.

Authors and Affiliations

1. Department of Computing, Curtin University of Technology, GPO Box U1987, Perth, Western Australia

   Sebastian Lühr, Svetha Venkatesh & Geoff West

2. Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, Menlo Park, CA

   Hung H. Bui

Editors and Affiliations

1. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

   Chengqi Zhang

2. Department of Computer Science, Auckland University of Technology, 1020, Auckland, New Zealand

   Hans W. Guesgen

3. Artificial Intelligence Technology Centre, Auckland University of Technology, Auckland, New Zealand

   Wai-Kiang Yeap

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© 2004 Springer-Verlag Berlin Heidelberg

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