Show simple item record

dc.contributor.authorÖzer, Sedat
dc.contributor.authorCirpan, Hakan Ali
dc.contributor.authorKabaoǧlu, Nihat
dc.description.abstractThis 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. © Springer-Verlag Berlin Heidelberg 2006.
dc.publisherSpringer Verlag
dc.titleSupport vector regression for surveillance purposes
dc.typeConference Paper
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.volume4105 LNCS
dc.contributor.khasauthorKabaoǧlu, Nihat

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record