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
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;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761045
