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.
|Number of pages||5|
|Journal||Proceedings - International Conference on Pattern Recognition|
|Publication status||Published - 2000|
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition