NDLI: Visual object tracking based on incremental kernel PCA

Content Provider	IEEE Xplore Digital Library
Author	Li Sun Guizhong Liu
Copyright Year	2010
Description	Author affiliation: School of Electronics and Information Engineering, Xi'an Jiaotong University (Li Sun; Guizhong Liu)
Abstract	This paper provides a novel method for visual object tracking based on incremental kernel principal component analysis. The proposed method is particularly robust in the case that the tracking object performs pose variation or there exists occasional occlusions. The whole method is constructed in the framework of particle filter and the state of object is defined by the position and shape of a parallelogram, with which the tracking result is located in every frame. For every particle, we compute its reconstructed preimage based on KPCA which can regressively estimate the de-noising pattern in the input space constructed by located objects in previous frames. The difference between the preimage and its original patch is finally adopted to measure the particle weight. Compared to the other state-of-the-art methods, the proposed method can cope with occasional occlusion and pose variation without significantly increasing the computation.
Starting Page	1
Ending Page	6
File Size	459895
Page Count	6
File Format	PDF
ISBN	9781424480289
ISSN	19493991
e-ISBN	9781424480296
e-ISBN	9781424480272
DOI	10.1109/CBMI.2010.5529895
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-23
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Shape Noise reduction Lighting Particle measurements Robustness Kernel Particle tracking Image reconstruction Principal component analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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