Object tracking in videos using adaptive mixture models and active contours

Authors:
Mohand Saïd Allili;Djemel Ziou
Affiliations:
Department of Computer Science, Faculty of Science, University of Sherbrooke, Sherbrooke, Quebec, Canada J1K 2R1;Department of Computer Science, Faculty of Science, University of Sherbrooke, Sherbrooke, Quebec, Canada J1K 2R1
Venue:
Neurocomputing
Year:
2008

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Abstract

In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods.