Multiple-view-based tracking of multiple humans

We propose a multiple-view-based tracking algorithm for multiple-human motions. In vision-based human tracking, self-occlusions and human-human occlusions are a part of the more significant problems. Employing multiple viewpoints and a viewpoint selection mechanism, however can reduce these problems. In our system, human positions are tracked with a sequence of multiple-viewpoint images. This tracking is based on the Kalman filtering approach. The estimation results are utilized to select proper viewpoints in other sub-tasks (rotation angle detection and body-side detection). Each sub-task has a different criterion for selecting viewpoints. We also describe the criterions for accomplishing individual sub-tasks and relationships between sub-tasks. We have already built an experimental system based on a small number of reliable image features. We confirm the stability of our algorithm through simulations. We also performed fundamental examinations on the experimental system.