5 August 2010 MAP spatial pyramid mean shift for object tracking
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Proceedings Volume 7744, Visual Communications and Image Processing 2010; 774435 (2010) https://doi.org/10.1117/12.863509
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China
Abstract
Mean Shift is popular in object tracking due to its simplicity and efficiency. It finds local maximum of the similarity measure between the target model and target candidate, and works well in many situations. However, it suffers from two aspects. First, Mean Shift tracker ignores background knowledge. As a result, it may fail when the background color is similar to that of the target or the initial target region contains too much background. Second, Mean Shift tracker omits the geometric structure with a global color histogram as the target model. Therefore, it may not work in the case of partial occlusion. To solve the first problem, we introduce background color histogram into a MAP formulation. To address the second problem, we divide the target into hierarchical blocks. These blocks are described with a histogram each but tracked as a whole. The two threads lead to a new algorithm, named MAP spatial pyramid (MAP-SP) Mean Shift. The efficiency of MAP-SP Mean Shift is demonstrated via comparative experiments on both standard and our own video sequences
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Xiaobo Han, Xiaobo Han, Peng Zhang, Peng Zhang, Houqiang Li, Houqiang Li, } "MAP spatial pyramid mean shift for object tracking", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774435 (5 August 2010); doi: 10.1117/12.863509; https://doi.org/10.1117/12.863509
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