Support vector regression for surveillance purposes

  • Authors:
  • Sedat Ozer;Hakan A. Cirpan;Nihat Kabaoglu

  • Affiliations:
  • Electrical & Electronics Engineering Department, Istanbul University, Avcilar, Istanbul;Electrical & Electronics Engineering Department, Istanbul University, Avcilar, Istanbul;Technical Vocational School of Higher Education, Kadir Has University, Selimpasa, Istanbul

  • Venue:
  • MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
  • Year:
  • 2006

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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 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.