Object tracking using genetic evolution based kernel particle filter

Authors:
Qicong Wang;Jilin Liu;Zhigang Wu
Affiliations:
Departmant of Information and Electronics Engineering, Zhejiang University, Hangzhou, Zhejiang, China;Departmant of Information and Electronics Engineering, Zhejiang University, Hangzhou, Zhejiang, China;College of Information & Electronics Engineering, Taizhou University, Linhai, Zhejiang, China
Venue:
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Year:
2006

Citing 4
Cited 1

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Extraction of Parametric Human Model for Posture Recognition Using Genetic Algorithm

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing

Swarm-supported outdoor localization with sparse visual data

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new particle filter, which combines genetic evolution and kernel density estimation, is proposed for moving object tracking. Particle filter (PF) solves non-linear and non-Gaussian state estimation problems in Monte Carlo simulation using importance sampling. Kernel particle filter (KPF) improves the performance of PF by using density estimation of broader kernel. However, it has the problem which is similar to the impoverishment phenomenon of PF. To deal with this problem, genetic evolution is introduced to form new filter. Genetic operators can ameliorate the diversity of particles. At the same time, genetic iteration drives particles toward their close local maximum of the posterior probability. Simulation results show the performance of the proposed approach is superior to that of PF and KPF.