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In this work, we present a multiple classifiers system cascades an on-line learning RGB-D appearance model framework in which detection, recognition, and tracking are highly coupled for a wheelchair robot equipped with a Kinect sensor to improve the efficiency of the care assistance and quality of accompanying service. The on-line trained classifiers use the surrounding background as negative examples...
In this paper, we present an approach to visual SLAM and human tracking for a wheelchair robot equipped with a Microsoft Kinect sensor that which is a novel sensing system that captures RGB and depth (RGB-D) images simultaneously. The speeded-up robust feature (SURF) algorithm is employed to provide the robust description of feature for environments and the target person from RGB images. Based on...
Human-robot interaction is an important issue for service robots. In this paper, we propose to utilize the multisensory data fusion to autonomously detect and track an accompanist as a target person in the surroundings of the mobile robot. First, we use laser range finder to detect the target person features which is characterized using distances and orientations from range sensor readings. Based...
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