Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Real-Time 3D Human Pose Estimation from Monocular View with Applications to Event Detection and Video Gaming

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

6 Author(s)
Ke, Shian-Ru ; Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA ; LiangJia Zhu ; Jenq-Neng Hwang ; Hung-I Pai
more authors

We present an effective real-time approach for automatically estimating 3D human body poses from monocular video sequences. In this approach, human body is automatically detected from video sequence, then image features such as silhouette, edge and color are extracted and integrated to infer 3D human poses by iteratively minimizing the cost function defined between 2D features derived from the projected 3D model and those extracted from video sequence. In addition, 2D locations of head, hands, and feet are tracked to facilitate 3D tracking. When tracking failure happens, the approach can detect and recover from failures quickly. Finally, the efficiency and robustness of the proposed approach is shown in two real applications: human event detection and video gaming.

Published in:

Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on

Date of Conference:

Aug. 29 2010-Sept. 1 2010