基于Kinect传感器骨架信息的手势识别人机交互

M. Rahim, Jungpil Shin, Md. Rashedul Islam
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引用次数: 10

摘要

手势为人机交互提供了一种自然、直观的交流媒介。因为,它可以用于虚拟现实、语言检测、电脑游戏等人机或人机指令应用。目前,基于传感器和摄像头的应用是许多研究人员感兴趣的领域。本文利用Kinect传感器的骨架数据提出了一种新的手势识别系统,该系统可以在人们不触摸设备或不进行口头交流的环境中工作。该模型主要关注两个模块,即手部区域和指尖检测和手势识别。通过定位掌心点来检测手部区域和指尖,找到轮廓的极值。通过测量人体骨骼信息的不同指标之间的距离来识别手势。这里考虑了六种手势指令,如从右向左移动,从左向右移动,从上到下移动,从下到上移动,打开和关闭,以及使用指尖识别数字。该系统能够检测手部区域和手指的存在,并识别不同的手势。实验结果表明,不同手势和伸长手指数的平均识别准确率分别为95.91%和96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human-Machine Interaction based on Hand Gesture Recognition using Skeleton Information of Kinect Sensor
The hand gesture provides a natural and intuitive communication medium for the human and machine interaction. Because, it can use in virtual reality, language detection, computer games, and other human-computer or human-machine instruction applications. Currently, the sensor and camera-based application is a field of interest for many researchers. This paper proposes a new hand gesture recognition system using the Kinect sensor's skeleton data, which works in an environment where people do not touch devices or communicate verbally. The proposed model focuses on mainly two modules, namely, hand area and fingertip detection, and hand gesture recognition. The hand area and fingertip are detected by positioning the palm point and find extreme of contour. And, the hand gesture is recognized by measuring the distance between different body indexes of skeleton information. Here, six gestures instructions are considered such as move right to left, move left to right, move up to down, move down to up, open and closed, and also recognize the numeric number using the fingertip. This system is able to detect the presence of hand area and fingers and to recognize different hand gestures. As a result, the average recognition accuracy of different hand gestures and stretched fingers numbers are 95.91% and 96%, respectively.
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