A new method is proposed for segmenting and tracking multiple objects through occlusion by integrating spatial-color Gaussian mixture model (SCGMM) into an energy function minimization framework. When occlusion does not occur, a SCGMM is learned for each object. When the objects are subject to occlusion, a multi-label energy function is formulated building on the learned 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.
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周桢. 基于多标记图分割的遮挡下多目标分割及跟踪算法[J]. 科学技术与工程, 2015, 15(10): . ZHOUZhen. A Multi-label Graph Cut-based Algorithm for Multi-object Segmentation and Tracking under Occlusion[J]. Science Technology and Engineering,2015,15(10).