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Proceedings Paper

Particle filtering with missing frames and its application to video tracking over lossy networks
Author(s): Jing Huang; Dan Schonfeld
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Paper Abstract

Many practical scenarios such as video tracking in lossy environment require a robust accurate tracking algorithm with dropped frames. A novel robust approach is proposed for visual tracking in the first part of this paper in the presence of frame loss with the Bayesian Importance Sampling framework based on first-order hidden Markov model (HMM). The graphical methods are firstly used to provide an exact solution for estimation using first-order hidden Markov model (HMM) with dropped frames. We subsequently rely on Sequential Importance Sampling to derive the first-order particle filtering algorithm with missing frames. In the second part of the paper, we promote this result and present that graphical methods can also be used to provide an exact solution to particle filtering with missing frames for an mth-order hidden Markov model (HMM) and cycle-free graphs. The resulting algorithm requires a small number of particles for efficient tracking. Experimental results demonstrate the superiority and robustness of the proposed approach to the standard methods, yet the additional computational time required is negligible.

Paper Details

Date Published: 31 January 2011
PDF: 8 pages
Proc. SPIE 7882, Visual Information Processing and Communication II, 78820F (31 January 2011); doi: 10.1117/12.872268
Show Author Affiliations
Jing Huang, Univ. of Illinois at Chicago (United States)
Dan Schonfeld, Univ. of Illinois at Chicago (United States)

Published in SPIE Proceedings Vol. 7882:
Visual Information Processing and Communication II
Amir Said; Onur G. Guleryuz; Robert L. Stevenson, Editor(s)

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