An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets
- 1 January 2004
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC in Lecture Notes in Computer Science
Abstract
No abstract availableKeywords
This publication has 16 references indexed in Scilit:
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Tracking Many Objects Using Subordinated CondensationPublished by British Machine Vision Association and Society for Pattern Recognition ,2002
- Probabilistic data association methods for tracking complex visual objectsIeee Transactions On Pattern Analysis and Machine Intelligence, 2001
- Automatically tracking and analyzing the behavior of live insect coloniesPublished by Association for Computing Machinery (ACM) ,2001
- Particle filters for state estimation of jump Markov linear systemsIEEE Transactions on Signal Processing, 2001
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian ModelsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2001
- Dynamic Conditional Independence Models and Markov Chain Monte Carlo MethodsJournal of the American Statistical Association, 1997
- Modeling a dynamic environment using a Bayesian multiple hypothesis approachArtificial Intelligence, 1994
- Tracking line segmentsImage and Vision Computing, 1990
- Monte Carlo sampling methods using Markov chains and their applicationsBiometrika, 1970