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IBPRIA
2009
Springer

Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagation

9 years 8 months ago
Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagation
We propose a novel statistical method for motion detection and background maintenance for a mobile observer. Our method is based on global motion estimation and statistical background modeling. In order to estimate the global motion, we use a Multiple Kernel Tracking combined with an adaptable model, formed by weighted histograms. This method is very light in terms of computation time and also in memory requirements, enabling the use of other methods more expensive, like belief propagation, to improve the final result. Key words: Real-time, Motion Detection, Background Subtraction, Mobile Observer, Multiple Kernel Tracking, Mosaicing, Belief Propagation, Markov Random Field
Marc Vivet, Brais Martínez, Xavier Binefa
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where IBPRIA
Authors Marc Vivet, Brais Martínez, Xavier Binefa
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