Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/67413
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Type: Conference paper
Title: Kernel-based tracking from a probabilistic viewpoint
Author: Nguyen, Q.
Robles-Kelly, A.
Shen, C.
Citation: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'07), 17-22 June, 2007; pp.1-8
Publisher: IEEE
Publisher Place: Online
Issue Date: 2007
ISBN: 1424411807
Conference Name: Computer Vision and Pattern Recognition (2007 : Minneapolis, MN, USA)
Statement of
Responsibility: 
Quang Anh Nguyen, Robles-Kelly, A. and Chunhua Shen
Abstract: In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixels in both, the target model and its candidate as random variables and make use of a generative model so as to cast the tracking task into a maximum likelihood framework. This, in turn, permits the use of the EM-algorithm to estimate a set of latent variables that can be used to update the target-center position. Once the latent variables have been estimated, we use the Kullback-Leibler divergence so as to minimise the mutual information between the target model and candidate distributions in order to develop a target-center update rule and a kernel bandwidth adjustment scheme. The method is very general in nature. We illustrate the utility of our approach for purposes of tracking on real-world video sequences using two alternative kernel functions.
Rights: © Copyright 2011 IEEE – All Rights Reserved
RMID: 0020112869
DOI: 10.1109/CVPR.2007.383240
Appears in Collections:Computer Science publications

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