Support Vector Regression for Surveillance Purposes

  • Sedat Ozer
  • Hakan A. Cirpan
  • Nihat Kabaoglu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


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 hyperplane 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.


Support Vector Machine Support Vector Support Vector Regression Target Tracking Support Vector Machine Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sedat Ozer
    • 1
  • Hakan A. Cirpan
    • 1
  • Nihat Kabaoglu
    • 2
  1. 1.Electrical & Electronics Engineering DepartmentIstanbul UniversityAvcilarIstanbul
  2. 2.Technical Vocational School of Higher EducationKadir Has UniversitySelimpasaIstanbul

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