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Proceedings Paper

Rao-Blackwellised particle filter with adaptive system noise and its evaluation for tracking in surveillance
Author(s): Xinyu Xu; Baoxin Li
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Paper Abstract

In the visual tracking domain, Particle Filtering (PF) can become quite inefficient when being applied into high dimensional state space. Rao-Blackwellisation [1] has been shown to be an effective method to reduce the size of the state space by marginalizing out some of the variables analytically [2]. In this paper based on our previous work [3] we proposed RBPF tracking algorithm with adaptive system noise model. Experiments using both simulation data and real data show that the proposed RBPF algorithm with adaptive noise variance improves its performance significantly over conventional Particle Filter tracking algorithm. The improvements manifest in three aspects: increased estimation accuracy, reduced variance for estimates and reduced particle numbers are needed to achieve the same level of accuracy.

Paper Details

Date Published: 19 January 2006
PDF: 8 pages
Proc. SPIE 6077, Visual Communications and Image Processing 2006, 60770W (19 January 2006); doi: 10.1117/12.643073
Show Author Affiliations
Xinyu Xu, Arizona State Univ. (United States)
Baoxin Li, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 6077:
Visual Communications and Image Processing 2006
John G. Apostolopoulos; Amir Said, Editor(s)

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