Automatic feature localisation with constrained local models
Pattern Recognition
Real time tracking using an active pan-tilt-zoom network camera
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
RSMAT: Robust simultaneous modeling and tracking
Pattern Recognition Letters
Face tracking with automatic model construction
Image and Vision Computing
Object tracking using learned feature manifolds
Computer Vision and Image Understanding
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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.