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Proceedings Paper

Vision-based posture recognition using an ensemble classifier and a vote filter
Author(s): Peng Ji; Changcheng Wu; Xiaonong Xu; Aiguo Song; Huijun Li
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Paper Abstract

Posture recognition is a very important Human-Robot Interaction (HRI) way. To segment effective posture from an image, we propose an improved region grow algorithm which combining with the Single Gauss Color Model. The experiment shows that the improved region grow algorithm can get the complete and accurate posture than traditional Single Gauss Model and region grow algorithm, and it can eliminate the similar region from the background at the same time. In the posture recognition part, and in order to improve the recognition rate, we propose a CNN ensemble classifier, and in order to reduce the misjudgments during a continuous gesture control, a vote filter is proposed and applied to the sequence of recognition results. Comparing with CNN classifier, the CNN ensemble classifier we proposed can yield a 96.27% recognition rate, which is better than that of CNN classifier, and the proposed vote filter can improve the recognition result and reduce the misjudgments during the consecutive gesture switch.

Paper Details

Date Published: 25 October 2016
PDF: 6 pages
Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101571J (25 October 2016); doi: 10.1117/12.2246542
Show Author Affiliations
Peng Ji, Southeast Univ. (China)
Changcheng Wu, Southeast Univ. (China)
Xiaonong Xu, Southeast Univ. (China)
Aiguo Song, Southeast Univ. (China)
Huijun Li, Southeast Univ. (China)

Published in SPIE Proceedings Vol. 10157:
Infrared Technology and Applications, and Robot Sensing and Advanced Control

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