Multiple object detection and tracking with pseudo-particle filter |
| |
Authors: | Baolong Guo and Wei Sun |
| |
Affiliation: | (1) School of Mechano-electronic Engineering, Xidian University, Xi’an, 710071, China |
| |
Abstract: | To tackle the divergence of the classical particle filter method for multiple object tracking in image sequences, a new particle
filter, called pseudoparticle filter (PPF), is proposed. The PPF invokes subset particles of generic particle filters to form
a continuous estimate of the posterior density function of the objects. After sampling-importance resampling (SIR), the subset
particles converge to the observations. It is proved that, using an appropriate kernel function of the mean shift algorithm,
we can get the subset particles of the observations and the fixed points of clustering results as the state of the objects.
A multiple object data association and state estimation technique is proposed to resolve the subset particles correspondence
ambiguities that arise when multiple objects are present. Experimental results demonstrate the efficiency and effectiveness
of the algorithm for single and multiple object tracking.
__________
Translated from Journal of Xidian University, 2008, 35(2): 248–253 译自: 西安电子科技大学学报(自然科学版)] |
| |
Keywords: | particle filter object recognition multi-object tracking image processing |
本文献已被 万方数据 SpringerLink 等数据库收录! |
|