Rao-Blackwell parçacık süzgeci ile 3 boyutlu insan takibi

Name: Rao-Blackwell parçacık süzgeci ile 3 boyutlu insan takibi
Author: Topçu, O., Orguner, U., Alatan, A. A., Ercan, Ali Özer

İsim	Rao-Blackwell parçacık süzgeci ile 3 boyutlu insan takibi
Yazar	Topçu, O., Orguner, U., Alatan, A. A., Ercan, Ali Özer
Basım Tarihi:	2014
Basım Yeri	- IEEE
Konu	Visual tracking, Rao-Blackwellization, Marginalization, Occlusion, Particle filter, Multi-camera
Tür	Belge
Dil	Türkçe
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4799-4874-1
Kayıt Numarası	a9d56609-4892-4c1a-8396-13a1062bb3d5
Lokasyon	Electrical & Electronics Engineering
Tarih	2014
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Visual tracking has an important place among computer vision applications. Visual tracking with particle filters is a well-known methodology. The performance of particle filters is dependent on efficient sampling of the state space, which in turn, is dependent on number of particles. In this paper, Rao-Blackwell technique is applied to particle filters to improve sampling efficiency. Both algorithms are applied to people tracking problem. Under the same circumstances, the resulting algorithm is demonstrated to perform better than the original algorithm via experiments on the PETS2009 benchmark dataset.
DOI	10.1109/SIU.2014.6830318

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Rao-Blackwell parçacık süzgeci ile 3 boyutlu insan takibi

Yazar Topçu, O., Orguner, U., Alatan, A. A., Ercan, Ali Özer

Basım Tarihi 2014

Basım Yeri - IEEE

Konu Visual tracking, Rao-Blackwellization, Marginalization, Occlusion, Particle filter, Multi-camera

Tür Belge

Dil Türkçe

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4799-4874-1

Kayıt Numarası a9d56609-4892-4c1a-8396-13a1062bb3d5

Lokasyon Electrical & Electronics Engineering

Tarih 2014

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Visual tracking has an important place among computer vision applications. Visual tracking with particle filters is a well-known methodology. The performance of particle filters is dependent on efficient sampling of the state space, which in turn, is dependent on number of particles. In this paper, Rao-Blackwell technique is applied to particle filters to improve sampling efficiency. Both algorithms are applied to people tracking problem. Under the same circumstances, the resulting algorithm is demonstrated to perform better than the original algorithm via experiments on the PETS2009 benchmark dataset.

DOI 10.1109/SIU.2014.6830318