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Robust object tracking using linear neighborhood propagation

[+] Author Affiliations
Chen Gong

Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Department of Automation, 800 DongChuan Road, 200240, China

Keren Fu

Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Department of Automation, 800 DongChuan Road, 200240, China

Enmei Tu

Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Department of Automation, 800 DongChuan Road, 200240, China

Jie Yang

Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Department of Automation, 800 DongChuan Road, 200240, China

Xiangjian He

University of Technology Sydney, School of Computing and Communications, Department of Computer Science, Broadway NSW 2007 2000, Australia,

J. Electron. Imaging. 22(1), 013015 (Jan 25, 2013). doi:10.1117/1.JEI.22.1.013015
History: Received June 9, 2012; Revised December 21, 2012; Accepted January 10, 2013
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Abstract.  Object tracking is widely used in many applications such as intelligent surveillance, scene understanding, and behavior analysis. Graph-based semisupervised learning has been introduced to deal with specific tracking problems. However, existing algorithms following this idea solely focus on the pairwise relationship between samples and hence could decrease the classification accuracy for unlabeled samples. On the contrary, we regard tracking as a one-class classification issue and present a novel graph-based semisupervised tracker. The proposed tracker uses linear neighborhood propagation, which aims to exploit the local information around each data point. Moreover, the manifold structure embedded in the whole sample set is discovered to allow the tracker to better model the target appearance, which is crucial to resisting the appearance variations of the object. Experiments on some public-domain sequences show that the proposed tracker can exhibit reliable tracking performance in the presence of partial occlusions, complicated background, and appearance changes, etc.

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© 2013 SPIE and IS&T

Citation

Chen Gong ; Keren Fu ; Enmei Tu ; Jie Yang and Xiangjian He
"Robust object tracking using linear neighborhood propagation", J. Electron. Imaging. 22(1), 013015 (Jan 25, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.1.013015


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