NDLI: Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints

Content Provider	IEEE Xplore Digital Library
Author	Ukita, Norimichi Hirai, Michiro Kidode, Masatsugu
Copyright Year	2009
Description	Author affiliation: Nara Institute of Science and Technology, Japan (Ukita, Norimichi; Hirai, Michiro; Kidode, Masatsugu)
Abstract	We propose a method for estimating the pose of a human body using its approximate 3D volume (visual hull) obtained in real time from synchronized videos. Our method can cope with loose-fitting clothing, which hides the human body and produces non-rigid motions and critical reconstruction errors, as well as tight-fitting clothing. To follow the shape variations robustly against erratic motions and the ambiguity between a reconstructed body shape and its pose, the probabilistic dynamical model of human volumes is learned from training temporal volumes refined by error correction. The dynamical model of a body pose (joint angles) is also learned with its corresponding volume. By comparing the volume model with an input visual hull and regressing its pose from the pose model, pose estimation can be realized. In our method, this is improved by double volume comparison: 1) comparison in a low-dimensional latent space with probabilistic volume models and 2) comparison in an observation volume space using geometric constrains between a real volume and a visual hull. Comparative experiments demonstrate the effectiveness of our method faster than existing methods.
Starting Page	1405
Ending Page	1412
File Size	756335
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459298
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Biological system modeling Humans Image reconstruction Clothing Joints Motion estimation Robustness Hidden Markov models Tracking
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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