By Topic

Estimation of Human Figure Motion Using Robust Tracking of Articulated Layers

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

2 Author(s)
Kooksang Moon ; Rutgers University ; Pavlovic, V.

We propose a probabilistic method for tracking articulated objects, such as the human figure, across multiple layers in monocular image sequence. In this method, each link of a probabilistic articulated object is assigned to one individual image layer. The layered representation allows us to robustly model the pose and occlusion of object parts during its motion. Appearance of links is described in terms of learned statistics of basic image features, such as color, and geometric models of robust spatial kernels. This results in a highly efficient computational method for inference of the object’s pose. We apply this approach to tracking of the human figure in monocular video sequences. We show that the proposed method, coupled with a learned dynamic model, can lead to a robust articulated object tracker.

Published in:

Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on

Date of Conference:

25-25 June 2005