Segmenting and tracking multiple objects under occlusion using multi-label graph cut

Authors:
Mingjun Wu;Xianrong Peng;Qiheng Zhang;Rujin Zhao
Affiliations:
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China;Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China;Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China;Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China
Venue:
Computers and Electrical Engineering
Year:
2010

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Abstract

This paper proposes a new method to segment and track multiple objects through occlusion by integrating spatial-color Gaussian mixture model (SCGMM) into an energy minimization framework. When occlusion does not occur, a SCGMM is learned for each object. When the objects are subject to occlusion, energy minimization is used to segment the objects from occlusion. To make the learned SCGMMs suitable for the segmentation of the current occlusion, a displacing procedure is utilized to adapt the SCGMMs to the spatial variations. A multi-label energy function is formulated building on the displaced SCGMMs and then minimized using the multi-label graph cut algorithm, thus leading to both the segmentation and tracking results of the objects with occlusion. Experimental validation of the proposed method is performed and presented on several video sequences.