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J4  2011, Vol. 45 Issue (12): 2240-2246    DOI: 10.3785/j.issn.1008-973X.2011.12.026
Weighted incremental subspace learning algorithm
suitable for object tracking
QIAN Cheng, ZHANG San-yuan
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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An object tracking method based on weighted incremental subspace learning was proposed to overcome the difficulty in object tracking resulting from variations in appearance. The method constructs a subspace updated online to depict the appearance of the object in the video. A set of image patches are predicted based on probabilistic transformation model as candidate image regions of the object in the current frame, then these image patches are projected onto the low-dimensional subspace, and the likelihood of each image patch as the image region of the object is evaluated. The image patch with maximal likelihood is regarded as the object image region. Finally, the subspace is updated incrementally with temporal weights. Experimental results show that the method accomplishes object tracking more steadily and accurately, compared with other incremental subspace learning based object tracking methods.

Published: 01 December 2011
CLC:  TP 391.41  
Cite this article:

QIAN Cheng, ZHANG San-yuan. Weighted incremental subspace learning algorithm
suitable for object tracking. J4, 2011, 45(12): 2240-2246.

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