Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/36020
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Type: Conference paper
Title: Classification-based likelihood functions for Bayesian tracking
Author: Shen, C.
Li, H.
Brooks, M.
Citation: IEEE International Conference on Video and Signal Based Surveillance, Nov. 2006:pp.33-33
Publisher: IEEE
Publisher Place: CDROM
Issue Date: 2006
ISBN: 0769526888
9780769526881
Conference Name: IEEE Conference on Video and Signal Based Surveillance (2006 : Sydney, Australia)
Statement of
Responsibility: 
Chunhua Shen; Hongdong Li; Brooks, M.J.
Abstract: The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well and those that do not. This paper describes a general framework for learning probabilistic models of objects for exploiting these models for tracking objects in image sequences. We use a discriminative classifier to learn models of how they appear in images. In particular, we use a support vector machine (SVM) for training, which is able to extract useful non-linear information, and thus represent more complex characteristics of the tracked object and background. This is a particular advantage when tracking deformable objects and where appearance changes due to the unstable illumination and pose occur. A by-product of the SVM training procedure is the classification function, with which the tracking problem is cast into a binary classification problem. An object detector directly using the classification function is then available. To make the tracker robust, an object detector that directly uses the classification function is combined into the tracker for object verification. This provides the capability for automatic initialisation and recovery from momentary tracking failures. We demonstrate improved robustness in image sequences.
Description: Copyright © 2006 IEEE
RMID: 0020062697
DOI: 10.1109/AVSS.2006.33
Appears in Collections:Computer Science publications

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