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Support vector regression for surveillance purposes

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Date
2006
Author
Özer, Sedat
Çırpan, Hakan Ali
Kabaoǧlu, Nihat
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.

Source

Multimedia Content Representation, Classification And Security

Volume

4105

Pages

442-449

URI

https://hdl.handle.net/20.500.12469/112

Collections

  • Araştırma Çıktıları / WOS [1518]
  • Elektrik-Elektronik Mühendisliği / Electrical - Electronics Engineering [320]
  • Teknik Bilimler Meslek Yüksekokulu [6]

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Contact Us | Send Feedback
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