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

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

^*Bu çalışma için yazışmadan sorumlu yazar

Istanbul University
Kadir Has University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Support vector machines based target tracking techniques
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2006 IEEE 14th Signal Processing and Communications Applications Conference
DOI'lar	https://doi.org/10.1109/SIU.2006.1659718
Yayın durumu	Yayınlandı - 2006
Harici olarak yayınlandı	Evet
Etkinlik	2006 IEEE 14th Signal Processing and Communications Applications - Antalya, Türkiye Süre: 17 Nis 2006 → 19 Nis 2006

Yayın serisi

Adı	2006 IEEE 14th Signal Processing and Communications Applications Conference
Hacim	2006

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2006 IEEE 14th Signal Processing and Communications Applications
Ülke/Bölge	Türkiye
Şehir	Antalya
Periyot	17/04/06 → 19/04/06

Belgeye Erişim

10.1109/SIU.2006.1659718

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@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, hac. 2006, 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Türkiye, 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; Hac. 2006).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

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.

UR - https://www.scopus.com/pages/publications/34247127418

U2 - 10.1109/SIU.2006.1659718

DO - 10.1109/SIU.2006.1659718

M3 - Konferans katkısı

AN - SCOPUS:34247127418

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap