Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/61894
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Type: Journal article
Title: Generalized kernel-based visual tracking
Author: Shen, C.
Kim, J.
Wang, H.
Citation: IEEE Transactions on Circuits and Systems for Video Technology, 2010; 20(1):119-130
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2010
ISSN: 1051-8215
1558-2205
Statement of
Responsibility: 
Chunhua Shen, Junae Kim, and Hanzi Wang
Abstract: Kernel-based mean shift (MS) trackers have proven to be a promising alternative to stochastic particle filtering trackers. Despite its popularity, MS trackers have two fundamental drawbacks: 1) the template model can only be built from a single image, and 2) it is difficult to adaptively update the template model. In this paper, we generalize the plain MS trackers and attempt to overcome these two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose. Compared with the plain MS tracker, it is now much easier to incorporate online template adaptation to cope with inherent changes during the course of tracking. To this end, a sophisticated online support vector machine is used. We demonstrate successful localization and tracking on various data sets.
Keywords: Global mode seeking; kernel-based tracking; mean shift; particle filter; support vector machine
Rights: Copyright 2010 IEEE
RMID: 0020100129
DOI: 10.1109/TCSVT.2009.2031393
Appears in Collections:Computer Science publications

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