Particle filtering with missing frames and its application to video tracking over lossy networks

Jing Huang; Dan Schonfeld

doi:10.1117/12.872268

31 January 2011 Particle filtering with missing frames and its application to video tracking over lossy networks

Jing Huang, Dan Schonfeld

Proceedings Volume 7882, Visual Information Processing and Communication II; 78820F (2011) https://doi.org/10.1117/12.872268
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

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.

Citation Download Citation

Jing Huang and Dan Schonfeld "Particle filtering with missing frames and its application to video tracking over lossy networks", Proc. SPIE 7882, Visual Information Processing and Communication II, 78820F (31 January 2011); https://doi.org/10.1117/12.872268

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KEYWORDS

Particles

Detection and tracking algorithms

Particle filters

Video

Electronic filtering

Optical tracking

Algorithm development

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