Multiple-human tracking using multiple cameras

We propose a human motion detection method using multiple-viewpoint images. 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. The vision system in this case should select the best viewpoints for extracting human motion information; the "best" selections can be changed among different types of target information. We address the problem of tracking human bodies. We divide the task into three primitive sub-tasks (position detection, rotation angle detection and body side detection). Each sub-task has a different criterion for selecting viewpoints and an estimation result of one sub-task can help another sub-task. We describe the criteria for accomplishing the individual sub-tasks and the relationships between sub-tasks. We have built an experimental system based on a small number of reliable image features and performed fundamental examinations on the viewpoint selection approach.