Destek vektör makineleri tabanli hedef takip yöntemleri

Sedat Özer; Hakan A. Çirpan; Nihat Kabaoǧlu

doi:10.1109/SIU.2006.1659718

Destek vektör makineleri tabanli hedef takip yöntemleri

Translated title of the contribution: Support vector machines based target tracking techniques

Sedat Özer^*, Hakan A. Çirpan, Nihat Kabaoǧlu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

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.

Translated title of the contribution	Support vector machines based target tracking techniques
Original language	Turkish
Title of host publication	2006 IEEE 14th Signal Processing and Communications Applications Conference
DOIs	https://doi.org/10.1109/SIU.2006.1659718
Publication status	Published - 2006
Externally published	Yes
Event	2006 IEEE 14th Signal Processing and Communications Applications - Antalya, Turkey Duration: 17 Apr 2006 → 19 Apr 2006

Publication series

Name	2006 IEEE 14th Signal Processing and Communications Applications Conference
Volume	2006

Conference

Conference	2006 IEEE 14th Signal Processing and Communications Applications
Country/Territory	Turkey
City	Antalya
Period	17/04/06 → 19/04/06

Access to Document

10.1109/SIU.2006.1659718

Cite this

@inproceedings{db54c727932e45429e821c65689374a4,

title = "Destek vekt{\"o}r makineleri tabanli hedef takip y{\"o}ntemleri",

abstract = "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.",

author = "Sedat {\"O}zer and {\c C}irpan, {Hakan A.} and Nihat Kabaoǧlu",

year = "2006",

doi = "10.1109/SIU.2006.1659718",

language = "T{\"u}rk{\c c}e",

isbn = "1424402395",

series = "2006 IEEE 14th Signal Processing and Communications Applications Conference",

booktitle = "2006 IEEE 14th Signal Processing and Communications Applications Conference",

note = "2006 IEEE 14th Signal Processing and Communications Applications ; Conference date: 17-04-2006 Through 19-04-2006",

}

Özer, S, Çirpan, HA & Kabaoǧlu, N 2006, Destek vektör makineleri tabanli hedef takip yöntemleri. in 2006 IEEE 14th Signal Processing and Communications Applications Conference., 1659718, 2006 IEEE 14th Signal Processing and Communications Applications Conference, vol. 2006, 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey, 17/04/06. https://doi.org/10.1109/SIU.2006.1659718

Destek vektör makineleri tabanli hedef takip yöntemleri. / Özer, Sedat; Çirpan, Hakan A.; Kabaoǧlu, Nihat.
2006 IEEE 14th Signal Processing and Communications Applications Conference. 2006. 1659718 (2006 IEEE 14th Signal Processing and Communications Applications Conference; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Destek vektör makineleri tabanli hedef takip yöntemleri

AU - Özer, Sedat

AU - Çirpan, Hakan A.

AU - Kabaoǧlu, Nihat

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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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DO - 10.1109/SIU.2006.1659718

M3 - Konferans katkısı

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SN - 1424402395

SN - 9781424402397

T3 - 2006 IEEE 14th Signal Processing and Communications Applications Conference

BT - 2006 IEEE 14th Signal Processing and Communications Applications Conference

T2 - 2006 IEEE 14th Signal Processing and Communications Applications

Y2 - 17 April 2006 through 19 April 2006

ER -

Destek vektör makineleri tabanli hedef takip yöntemleri

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