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Conference papers
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.
Cited literature [22 references]
https://hal.inria.fr/inria-00325617
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
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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VS2008-Oral-3b.pdf
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