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3D human tracking with Rao-Blackwell particle filter

Title	3D human tracking with Rao-Blackwell particle filter
Author	Topçu, O., Orguner, U., Alatan, A. A., Ercan, Ali Özer
Publication Date:	2014
Publication Place	- IEEE
Subject	Visual tracking, Rao-Blackwellization, Marginalization, Occlusion, Particle filter, Multi-camera
Type	Document
Language	Turkish
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-4799-4874-1
Record ID	a9d56609-4892-4c1a-8396-13a1062bb3d5
Library Location	Electrical & Electronics Engineering
Date	2014
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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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Özyeğin University

3D human tracking with Rao-Blackwell particle filter

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

Publication Date 2014

Publication Place - IEEE

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

Type Document

Language Turkish

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-4799-4874-1

Record ID a9d56609-4892-4c1a-8396-13a1062bb3d5

Library Location Electrical & Electronics Engineering

Date 2014

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

Sample Text 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