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Dynamic ensemble for target tracking

Abstract : On-line boosting is a recent breakthrough in the machine learning literature that has opened new possibilities in many diverse fields. Instead of generating a static strong classifier off-line, the classifier can be built on-the-fly on incoming samples. This has been succesfully exploited in treating computer vision tasks such as tracking as a classification problem thus providing an intriguing new perspective to an old subject. Known solutions to the on-line boosting problem rely on a fixed number of weak classifiers. The first main contribution of this paper removes this limitation and shows how a dynamic ensemble can better address the tracking problem by providing increased robustness. The second proposed novelty consists in a mechanism for detecting scale changes of tracked targets. Promising results are shown on publicly available and our own video sequences.
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Contributor : Peter Sturm <>
Submitted on : Monday, September 29, 2008 - 5:33:06 PM
Last modification on : Monday, September 29, 2008 - 10:01:50 PM
Long-term archiving on: : Friday, June 4, 2010 - 11:56:43 AM


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  • HAL Id : inria-00325617, version 1



Ingrid Visentini, Lauro Snidaro, Gian Luca Foresti. Dynamic ensemble for target tracking. The Eighth International Workshop on Visual Surveillance - VS2008, Graeme Jones and Tieniu Tan and Steve Maybank and Dimitrios Makris, Oct 2008, Marseille, France. ⟨inria-00325617⟩



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