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Published Articles >> Table of Contents >> Abstract
2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06)
p. 33
Classification-Based Likelihood Functions for Bayesian Tracking
Chunhua Shen, National ICT Australia, Australia; Australian National University, Australia
Hongdong Li, National ICT Australia, Australia; Australian National University, Australia
Michael J. Brooks, University of Adelaide, Australia
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AVSS.2006.33
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| Abstract |
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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.
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 Additional Information
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Citation:
Chunhua Shen, Hongdong Li, Michael J. Brooks,
"Classification-Based Likelihood Functions for Bayesian Tracking,"
avss,
p. 33,
2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06),
2006
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