Simultaneous Modeling and Tracking (SMAT) of Feature Sets

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 class 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, this is a robust tracker that operates in realtime without prior data.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: N. D. H. Dowson, Richard Bowden
• Keywords: Computer Vision, CVPR 2005, Exemplar Based Approach, Feature Level, Feature Positions, Prior Data, Priori Data