3D People Tracking Using the Particle Filter with Cascaded Classifiers

Yoshinori KOBAYASHI  Daisuke SUGIMURA  Kousuke HIRASAWA  Naohiko SUZUKI  Hiroshi KAGE  Yoichi SATO  Akihiro SUGIMOTO 

Publication
D - Abstracts of IEICE TRANSACTIONS on Information and Systems (Japanese Edition)  Vol.J90-D  No.8  pp.2049-2059
Publication Date: 2007/08/01
Online ISSN: 1881-0225
Print ISSN: 1880-4535
Type of Manuscript: Special Section PAPER (Special Section on Image Recognition and Understanding)
Category: 
Keyword: 
people tracking,  multiple cameras,  particle filter,  AdaBoost, 

Full Text(in Japanese): PDF(1.4MB)

Summary: 
In the last decade, a variety of tracking techniques based on particle filters have been proposed. However, most of the existing methods adopt only simple perceptual cues such as color histograms or contour similarities for hypothesis evaluation. To improve the robustness and accuracy of tracking, it is very important to have a better means for hypothesis evaluation. In this work, we present a novel hypothesis evaluation technique for human head tracking using cascaded classifiers based on AdaBoost and Haar-like features. In particular, we use multiple classifiers, each of which is trained for detecting human heads of a particular direction. Among these classifiers, the most suitable one is selected adaptively by considering each hypothesis and known camera position. Our experimental results demonstrate the effectiveness and robustness of our method.