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 language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Pattern Recognition |
| Pages | 3476-3479 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey Duration: 2010 Aug 23 → 2010 Aug 26 |
Other
| Other | 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
|---|---|
| Country | Turkey |
| City | Istanbul |
| Period | 10/8/23 → 10/8/26 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
3D human body modeling using range data. / Yamauchi, Koichiro; Bhanu, Bir; Saito, Hideo.
Proceedings - International Conference on Pattern Recognition. 2010. p. 3476-3479 5597543.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - 3D human body modeling using range data
AU - Yamauchi, Koichiro
AU - Bhanu, Bir
AU - Saito, Hideo
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78149490617&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149490617&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.849
DO - 10.1109/ICPR.2010.849
M3 - Conference contribution
AN - SCOPUS:78149490617
SN - 9780769541099
SP - 3476
EP - 3479
BT - Proceedings - International Conference on Pattern Recognition
ER -