Teknik Bilimler Meslek Yüksekokulu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/2671

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Browsing Teknik Bilimler Meslek Yüksekokulu by Department "Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü"

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    Support vector machines based target tracking techniques
    (IEEE, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. 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 using 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 demostrated in a sensor network scenario with a moving target in a polynomial route.
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    Conference Object
    Citation Count: 1
    Support vector machines based target tracking techniques [Destek vektör makineleri tabanlı hedef takip yöntemleri]
    (2006) Özer, Sedat; Cirpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. 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 using 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 demostrated in a sensor network scenario with a moving target in a polynomial route. © 2006 IEEE.
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    Article
    Citation Count: 1
    Support vector regression for surveillance purposes
    (Springer-Verlag Berlin, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat
    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.