DocumentCode
2473811
Title
An MCMC-based particle filter for multiple person tracking
Author
Zuriarrain, I. ; Lerasle, Frederic ; Arana, N. ; Devy, Michel
Author_Institution
Univ. of Mondragon, Spain
fYear
2008
fDate
8-11 Dec. 2008
Firstpage
1
Lastpage
4
Abstract
This paper presents a Markov Chain Monte Carlo (MCMC) based particle filter to track multiple persons dedicated to video surveillance applications. This hybrid tracker, devoted to networked intelligent cameras, takes benefit from the best properties of both MCMC and joint particle filter. A saliency map-based proposal distribution is shown to limit the well-known burst in terms of particles and MCMC iterations. Qualitative and quantitative results for real-world video data are presented.
Keywords
Markov processes; Monte Carlo methods; particle filtering (numerical methods); target tracking; video surveillance; Markov chain Monte Carlo based particle filter; multiple person tracking; networked intelligent cameras; video surveillance; Field programmable gate arrays; Humans; Monte Carlo methods; Particle filters; Particle tracking; Proposals; Smart cameras; State estimation; Target tracking; Video surveillance;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location
Tampa, FL
ISSN
1051-4651
Print_ISBN
978-1-4244-2174-9
Electronic_ISBN
1051-4651
Type
conf
DOI
10.1109/ICPR.2008.4761045
Filename
4761045
Link To Document