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 |
|---|---|
| Pages (from-to) | 693-697 |
| Number of pages | 5 |
| Journal | Proceedings - International Conference on Pattern Recognition |
| Volume | 15 |
| Issue number | 4 |
| Publication status | Published - 2000 Dec 1 |
| Externally published | Yes |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Fingerprint Dive into the research topics of 'Remarks on a real-time 3d human body posture estimation method using trinocular images'. Together they form a unique fingerprint.
Cite this
Takahashi, K., Sakaguchi, T., & Ohya, J. (2000). Remarks on a real-time 3d human body posture estimation method using trinocular images. Proceedings - International Conference on Pattern Recognition, 15(4), 693-697.