This paper proposes a Kinect-based calling gesture recognition scenario for taking order service of an elderly care robot. The proposed scenarios are designed mainly for helping non expert users like elderly to call service robot for their service request. In order to facilitate elderly service, natural calling gestures are designed to interact with the robot. Our challenge here is how to make the natural calling gesture recognition work in a cluttered and randomly moving objects. In this approach, there are two modes of our calling gesture recognition: Skeleton based gesture recognition and Octree based gesture recognition. Individual people is segmented out from 3D point cloud acquired by Microsoft Kinect, skeleton is generated for each segment, face detection is applied to identify whether the segment is human or not, specific natural calling gestures are designed based on skeleton joints. For the case that user is sitting on a chair or sofa, correct skeleton cannot be generated, Octree based gesture recognition procedure is used to recognize the gesture, in which human segments with head and hand are identified by face detection as well as specific geometrical constrains and skin color evidence. The proposed method has been implemented and tested on "HomeMate", a service robot developed for elderly care. The performance and results are given.
This paper presents a method of calling gesture recognition by isolating the head and hand of a caller based on octree segmentation. The recognition of calling gestures is designed here mainly for elderly to call a service robot for their service request. A big challenge to solve is how to make the calling gesture recognition work in a complex environment with crowded people, cluttered and randomly moving objects, as well as illumination variations. The approach taken here is to segment out individual people from the 3D point cloud acquired by Microsoft Kinect or ASUS Xtion Pro and detect their heads and hands in certain geometric configurations. The segmentation is done fast by representing the 3D point cloud in octree cells and clustering those octree cells connected by the neighborhood relationship. The head and hand in a certain geometric configuration are identified from the candidate regions defined with a segmented object and by detecting the shape and color evidences. Color model in HSV color space also discussed to well define the skin color model. The proposed method has been implemented and tested on "HomeMate," a service robot developed for elderly care. The result of performance evaluation is given.
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