Support vector regression for surveillance purposes

Sedat Ozer*, Hakan A. Cirpan, Nihat Kabaoglu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationMultimedia Content Representation, Classification and Security - International Workshop, MRCS 2006. Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540393927, 9783540393924
Publication statusPublished - 2006
Externally publishedYes
EventInternational Workshop on Multimedia Content Representation, Classification and Security, MRCS 2006 - Istanbul, Turkey
Duration: 11 Sept 200613 Sept 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4105 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Workshop on Multimedia Content Representation, Classification and Security, MRCS 2006


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