We propose a method of tracking 3D position, posture, and shapes of human hands from multiple-viewpoint images. Self-occlusion and hand-hand occlusion are serious problems in the vision-based hand tracking. Our system employs multiple-viewpoint and viewpoint selection mechanism to reduce these problems. Each hand position is tracked with a Kalman filter and the motion vectors are updated with image features in selected images that do not include hand-hand occlusion. 3D hand postures are estimated with a small number of reliable image features. These features are extracted based on distance transformation, and they are robust against changes in hand shape and self-occlusion. Finally, a `best view' image is selected for each hand for shape recognition. The shape recognition process is based on a Fourier descriptor. Our system can be used as a user interface device in a virtual environment, replacing glove-type devices and overcoming most of the disadvantages of contact-type devices.
|Number of pages||6|
|Journal||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publication status||Published - 1999 Jan 1|
|Event||Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA|
Duration: 1999 Jun 23 → 1999 Jun 25
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition