Article ID Journal Published Year Pages File Type
527430 Image and Vision Computing 2007 10 Pages PDF
Abstract

In this paper, we present an approach for recognizing pointing gestures in the context of human–robot interaction. In order to obtain input features for gesture recognition, we perform visual tracking of head, hands and head orientation. Given the images provided by a calibrated stereo camera, color and disparity information are integrated into a multi-hypothesis tracking framework in order to find the 3D-positions of the respective body parts. Based on the hands’ motion, an HMM-based classifier is trained to detect pointing gestures. We show experimentally that the gesture recognition performance can be improved significantly by using information about head orientation as an additional feature. Our system aims at applications in the field of human–robot interaction, where it is important to do run-on recognition in real-time, to allow for robot egomotion and not to rely on manual initialization.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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