By Topic

Use hierarchical genetic particle filter to figure articulated human tracking

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Long Ye ; Inf. Eng. Sch., Commun. Univ. of China, Beijing ; Qin Zhang ; Ling Guan

Using particle filter to track human movement, a key problem is how to draw samples in high-dimensional state space. In this paper, we present a novel framework of particle filtering, namely Hierarchical Genetic Particle Filter (HGPF), to improve the efficiency of samples by a hierarchical evolutionary detection. As a result, we can obtain reasonably distributed samples thus translating into reliable tracking performance. Finally, we apply the technique to 2D articulated human movement tracking. Result demonstrates the effectiveness of HGPF in solving the tracking problem like self-occlusion and cluttered background.

Published in:

Multimedia and Expo, 2008 IEEE International Conference on

Date of Conference:

June 23 2008-April 26 2008