We propose a method for real-time people tracking using multiple cameras. The particle filter framework is known to be effective for tracking people, but most of existing methods adopt only simple perceptual cues such as color histogram or contour similarity for hypothesis evaluation. To improve the robustness and accuracy of tracking more sophisticated hypothesis evaluation is indispensable. We therefore present a novel technique for human head tracking using cascaded classifiers based on AdaBoost and Haar-like features for hypothesis evaluation. In addition, we use multiple classifiers, each of which is trained respectively to detect one direction of a human head. During real-time tracking the most suitable classifier is adaptively selected by considering each hypothesis and known camera position. Our experimental results demonstrate the effectiveness and robustness of our method.
This paper describes the development of a system for implementing a new concept that we call "bedside Wellness." The system aims at improving the quality of life for bedridden patients and the elderly. A prototype system intending to reduce stress and facilitate movement was proposed and developed based on a basic study. It provides a virtual walk through a forest by using VR (virtual reality) technology and foot devices installed on the bed. The system was evaluated by an experiment with healthy subjects. The data suggested positive effects of the system; however, points for improvement were also learned. After making some of these improvements, we will begin clinical use of the system.
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