Abstract
This paper proposes a new real-time method of estimating human postures in 3D from trinocular images. The proposed method extracts feature points of the human body by applying a type of function analysis to contours of human silhouettes. To overcome self-occlusion problems, dynamic compensation is carried out using the Kalman filter and all feature points are tracked. The 3D coordinates of the feature points are reconstructed by considering the geometrical relationship between the three cameras. Experimental results confirm both the feasibility and the effectiveness of the proposed method, and an application example of the 3D human body posture estimation to a motion recognition system is presented.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Pattern Recognition |
| Pages | 693-697 |
| Number of pages | 5 |
| Volume | 15 |
| Edition | 4 |
| Publication status | Published - 2000 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Hardware and Architecture
Cite this
Remarks on a real-time 3d human body posture estimation method using trinocular images. / Takahashi, Kazuhiko; Sakaguchi, Tatsumi; Ohya, Jun.
Proceedings - International Conference on Pattern Recognition. Vol. 15 4. ed. 2000. p. 693-697.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Remarks on a real-time 3d human body posture estimation method using trinocular images
AU - Takahashi, Kazuhiko
AU - Sakaguchi, Tatsumi
AU - Ohya, Jun
PY - 2000
Y1 - 2000
N2 - This paper proposes a new real-time method of estimating human postures in 3D from trinocular images. The proposed method extracts feature points of the human body by applying a type of function analysis to contours of human silhouettes. To overcome self-occlusion problems, dynamic compensation is carried out using the Kalman filter and all feature points are tracked. The 3D coordinates of the feature points are reconstructed by considering the geometrical relationship between the three cameras. Experimental results confirm both the feasibility and the effectiveness of the proposed method, and an application example of the 3D human body posture estimation to a motion recognition system is presented.
AB - This paper proposes a new real-time method of estimating human postures in 3D from trinocular images. The proposed method extracts feature points of the human body by applying a type of function analysis to contours of human silhouettes. To overcome self-occlusion problems, dynamic compensation is carried out using the Kalman filter and all feature points are tracked. The 3D coordinates of the feature points are reconstructed by considering the geometrical relationship between the three cameras. Experimental results confirm both the feasibility and the effectiveness of the proposed method, and an application example of the 3D human body posture estimation to a motion recognition system is presented.
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UR - http://www.scopus.com/inward/citedby.url?scp=0006669393&partnerID=8YFLogxK
M3 - Chapter
AN - SCOPUS:0006669393
VL - 15
SP - 693
EP - 697
BT - Proceedings - International Conference on Pattern Recognition
ER -