Support vector regression for surveillance purposes

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Date

2006

Authors

Özer, Sedat
Çırpan, Hakan Ali
Kabaoğlu, Nihat

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Springer-Verlag Berlin

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Abstract

This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers to use dynamic model together as feature vectors and makes the byperplane and the support vectors follow the changes in these features. The performance of the tracker is demonstrated in a sensor network scenario with a constant velocity moving target on a plane for surveillance purpose.

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1

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N/A

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Q2

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Volume

4105

Issue

Start Page

442

End Page

449