Sciweavers

Share
ICMCS
2007
IEEE

Variable Number of "Informative" Particles for Object Tracking

9 years 2 months ago
Variable Number of "Informative" Particles for Object Tracking
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on two key factors: how many particles are used and how these particles are re-located. In this paper, we estimate the number of required particles using the Kullback-Leibler distance (KLD), which is called KLD-sampling, and we use a hybrid dynamic model to generate diversified particles, which suits object’s agile motion. Besides, we employ the mean shift analysis as a local mode seeking mechanism to make each particle more “informative”. We demonstrate the performance of the proposed algorithm tracking the ball in sports video clips.
Yu Huang, Joan Llach
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICMCS
Authors Yu Huang, Joan Llach
Comments (0)
books