Distributed Markov Chain Monte Carlo kernel based particle filtering for object tracking

Authors:
Danling Wang;Qin Zhang;John Morris
Affiliations:
School of Information and Engineering, Communication University of China, Beijing, China;School of Information and Engineering, Communication University of China, Beijing, China;Department of Computer Science, The University of Auckland, Auckland, New Zealand
Venue:
Multimedia Tools and Applications
Year:
2012

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Abstract

Particle filters are computationally intensive and thus efficient parallelism is crucial to effective implementations, especially object tracking in video sequences. Two schemes for pipelining particles under high performance computing environment, including an alternative Markov Chain Monte Carlo (MCMC) resampling algorithm and kernel function, are proposed so as to improve tracking performance and minimize execution time. Experimental results on a network of workstations composed of simple off-the-shelf hardware components show that global parallelizable scheme provides a promising resolution to clearly reduce execution time with increasing particles, compared with generic particle filtering.