Deformable object shape refinement and tracking using graph cuts and support vector machines

Authors:
Mehmet Kemal Kocamaz;Yan Lu;Christopher Rasmussen
Affiliations:
Department of Computer and Information Sciences, University of Delaware, Newark, DE;Department of Computer and Information Sciences, University of Delaware, Newark, DE;Department of Computer and Information Sciences, University of Delaware, Newark, DE
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Year:
2011

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Abstract

This paper describes several approaches to the problem of obtaining a refined segmentation of an object given a coarse initial segmentation of it. One line of investigation modifies the standard graph cut method by incorporating color and shape distance terms, adaptively weighted at run time to try to favor the most informative cue given visual conditions. We also discuss a machine learning approach based on support vector machines which uses color and spatial features as well. Furthermore, we extend these single-frame refinement methods to serve as the basis of trackers which work for a variety of object types with complex, deformable shapes. Comparative results are presented for several diverse datasets including objects such as trail regions used for robot navigation, hands, and faces.