Thursday 28th of March 2024
 

Hand Gesture Recognition and Its Application in Robot Control


Pei-Guo Wu and Qing-Hu Meng

In view of the problem that the accuracy and robustness of hand gesture recognition technology based on vision in the process of using gestures to interact with robots were unstable due to its background, illumination and other factors, this paper presented a gesture segmentation and recognition method Combining depth information and color images. First, it used Kinect sensor to obtain depth information and color images, then used depth information to pick the hand part from color images, and then got gesture images through color segmentation method. Second, it calculated HU invariant moments and shape features of the gesture images as feature information. Finally, it used the feature information to train the support vector machine, then implemented hand gesture recognition for static hand gestures. Experimental results show that the method has strong robustness to the influence of background interference, illumination variation, translation, rotation and zoom, and can be applied to control intelligent robot.

Keywords: Kinect; hand gesture recognition; Hu invariant moments; support vector machine; robot.

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ABOUT THE AUTHORS

Pei-Guo Wu
male, born in 1991, is a master candidate in computer application technology at the Information Engineering College, Henan University of Science and Technology, Luoyang, China. His research interests include image processing and recognition.

Qing-Hu Meng
male, born in 1962, received the Master¡¯s degree from Beijing Institute of Technology, Beijing, China, in 1988, and the Ph.D. degree in electrical and computer engineering from the University of Victoria, BC, Canada, in 1992. He was a Professor in the Department of Electrical and Computer Engineering at the University of Alberta, Canada, from April 1994 to August 2004. Currently, he is a Professor with the Department of Electronic Engineering, Chinese University of Hong Kong. His research interests are in the areas of biomedical engineering, medical and surgical robotics, active capsule endoscopy, medical image-based automatic diagnosis, interactive telemedicine and telehealthcare, biosensors and multisensor data fusion, bio-MEMS with medical applications, biomedical devices and robotic assistive technologies and prosthetics, adaptive and intelligent systems, and related medical and industrial applications.


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