3D human body modeling using range data

Koichiro Yamauchi, Bir Bhanu, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For the 3D modeling of walking humans the determination of body pose and extraction of body parts, from the sensed 3D range data, are challenging image processing problems. Real body data may have holes because of self-occlusions and grazing angle views. Most of the existing modeling methods rely on direct fitting a 3D model into the data without considering the fact that the parts in an image are indeed the human body parts. In this paper, we present a method for 3D human body modeling using range data that attempts to overcome these problems. In our approach the entire human body is first decomposed into major body parts by a parts-based image segmentation method, and then a kinematics model is fitted to the segmented body parts in an optimized manner. The fitted model is adjusted by the iterative closest point (ICP) algorithm to resolve the gaps in the body data. Experimental results and comparisons demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages3476-3479
Number of pages4
DOIs
Publication statusPublished - 2010 Nov 18
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 2010 Aug 232010 Aug 26

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period10/8/2310/8/26

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Yamauchi, K., Bhanu, B., & Saito, H. (2010). 3D human body modeling using range data. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 3476-3479). [5597543] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.849