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Classification-based likelihood functions for Bayesian tracking

Shen, Chunhua; Li, Hongdong; Brooks, Michael

Description

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...[Show more]

CollectionsANU Research Publications
Date published: 2006
Type: Conference paper
URI: http://hdl.handle.net/1885/51579
Source: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
DOI: 10.1109/AVSS.2006.33

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