Mixture Hyperplanes Approximation for Global Tracking

Song GU  Zheng MA  Mei XIE  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.11   pp.2008-2012
Publication Date: 2015/11/01
Publicized: 2015/08/13
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8040
Type of Manuscript: LETTER
Category: Pattern Recognition
template tracking,  Mixture Hyperplanes Approximation,  fast learning,  regression,  

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Template tracking has been extensively studied in Computer Vision with a wide range of applications. A general framework is to construct a parametric model to predict movement and to track the target. The difference in intensity between the pixels belonging to the current region and the pixels of the selected target allows a straightforward prediction of the region position in the current image. Traditional methods track the object based on the assumption that the relationship between the intensity difference and the region position is linear or non-linear. They will result in bad tracking performance when just one model is adopted. This paper proposes a method, called as Mixture Hyperplanes Approximation, which is based on finite mixture of generalized linear regression models to perform robust tracking. Moreover, a fast learning strategy is discussed, which improves the robustness against noise. Experiments demonstrate the performance and stability of Mixture Hyperplanes Approximation.