This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Controlled human pose estimation from depth image streams
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Youding Zhu, The Ohio State Univ., Dreese Laborartory 395, 2015 Neil Ave., Columbus 43210, USA
Behzad Dariush, Honda Research Institute USA, 800 California St. Suite 300, Mountain View 94041, USA
Kikuo Fujimura, Honda Research Institute USA, 800 California St. Suite 300, Mountain View 94041, USA
This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from features detected using depth images obtained from a time of flight imaging device. The features represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm. The detected features are subsequently used as input to a constrained, closed loop tracking control algorithm which not only estimates the pose of the articulated human model, but also provides feedback to the feature detector in order to resolve ambiguities or to provide estimates of undetected features. Based on a simple kinematic model, constraints such as joint limit avoidance, and self penetration avoidance are enforced within the tracking control framework. We demonstrate the effectiveness of the algorithm with experimental results of upper body pose reconstruction from a small set of features. On average, the entire pipeline runs at approximately 10 frames per second on a standard 3 GHz PC using a 17 degree of freedom upper body human model.
Citation:
Youding Zhu, Behzad Dariush, Kikuo Fujimura, "Controlled human pose estimation from depth image streams," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
Usage of this product signifies your acceptance of the Terms of Use.