Efficient particle filter-based tracking of multiple interacting targets using an mrf-based motion model
- 1 October 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 254-259
- https://doi.org/10.1109/iros.2003.1250637
Abstract
We describe a multiple hypothesis particle filter for tracking targets that are influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.Keywords
This publication has 13 references indexed in Scilit:
- Learning flexible sprites in video layersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Monte Carlo localization for mobile robotsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Probabilistic data association methods for tracking complex visual objectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2001
- Automatically tracking and analyzing the behavior of live insect coloniesPublished by Association for Computing Machinery (ACM) ,2001
- Markov Random Field Modeling in Computer VisionPublished by Springer Nature ,1995
- Image Analysis, Random Fields and Dynamic Monte Carlo MethodsPublished by Springer Nature ,1995
- Modeling a dynamic environment using a Bayesian multiple hypothesis approachArtificial Intelligence, 1994
- Tracking line segmentsImage and Vision Computing, 1990
- Sonar tracking of multiple targets using joint probabilistic data associationIEEE Journal of Oceanic Engineering, 1983