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A. Galata and N. Johnson and D. C. Hogg
ABSTRACT
In recent years there has been an increased interest in the modelling and recognition of human activities involving highly structured and semantically rich behaviour such as dance, aerobics, and sign language. A novel approach is presented for automatically acquiring stochastic models of the high-level structure of an activity without the assumption of any prior knowledge. The process involves temporal segmentation into plausible atomic behaviour components and the use of variable length Markov models for the e cient representation of behaviours. Experimental results are presented which demonstrate the synthesis of realistic sample behaviours and the performance of models for long-term temporal prediction. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Learning variable length Markov models of behaviourSite generated on Friday, 06 January 2006