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Simultaneous modeling and tracking (SMAT) of feature sets

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2 Author(s)
Dowson, N.D.H. ; Centre for Speech Vision & Signal Process., Surrey Univ., Guildford, UK ; Bowden, R.

A novel method for the simultaneous modeling and tracking (SMAT) of a feature set during motion sequence is proposed. The method requires no prior information. Instead the a posteriori distribution of appearance and shape is built up incrementally using an exemplar based approach. The resulting model is less optimal than when a priori data is used, but can be built in real-time. Data in any form may be used, provided a distance measure and a means to classify outliers exists. Here, a two tier implementation of SMAT is used: at the feature level, mutual information is used to track image patches; and at the object level, a structure model is built from the feature positions. As experiments demonstrate, the tracker is robust and operates in real-time without requiring prelearned data.

Published in:

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on  (Volume:2 )

Date of Conference:

20-25 June 2005