Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling an...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2009-05, Vol.31 (5), p.919-930
Hauptverfasser:	Bohyung Han, Ying Zhu, Comaniciu, D., Davis, L.S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial Intelligence Bayes Theorem Bayesian filtering Bayesian methods Computer science control theory systems Costs Density density approximation Density functional theory density interpolation density propagation Dynamical systems Exact sciences and technology Filtering Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Interpolation Mathematical analysis mean shift Monte Carlo methods Nonlinear dynamics particle filter Particle filters Particle tracking Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Probability density function Reproducibility of Results Sampling Sampling methods Sensitivity and Specificity Signal Processing, Computer-Assisted Subtraction Technique Theoretical computing visual tracking
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