Variable-mass particle filter for road-constrained vehicle tracking

Authors:
Giorgos Kravaritis;Bernard Mulgrew
Affiliations:
Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK;Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

Citing 2
Cited 2

Real time classification and tracking of multiple vehicles in highways

Pattern Recognition Letters
Particle filters for positioning, navigation, and tracking

IEEE Transactions on Signal Processing

A Multi-modal Particle Filter Based Motorcycle Tracking System

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Tracking human pose with multiple activity models

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper studies the road-constrained vehicle tracking problem employing the multiple-model particle filtering framework. It introduces an approach which enables for a more efficient particle use within the multimodel structure of the tracker; rather than allocating the particles to the various modes of operation using fixed mode probabilities, it proposes to allocate the particles freely according to user-defined application-specific criteria. For compensating for the arbitrary allocation of the particles, the particles are assigned with masses which scale appropriately their weights. Simulation results demonstrate the improved particle efficiency of the new variable-mass approach when contrasted with the standard variable-structure multiple model particle filter in a vehicle tracking application.