计算机与现代化

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基于RGBD数据的静态手势识别

  

  1. 长安大学信息工程学院,陕西-西安-710064
  • 收稿日期:2017-05-10 出版日期:2018-01-23 发布日期:2018-01-24
  • 作者简介:文芳(1992-),女,山东菏泽人,长安大学信息工程学院硕士研究生,研究方向:数字图像处理; 康彩琴(1992-),女,陕西延安人,硕士研究生,研究方向:图形图像处理。 [ZK)][HJ][HT][FQ)]

Static Hand Gesture Recognition Based on RGBD Data

  1. School of Information Engineering, Chang’an University, Xi’an 710064, China
  • Received:2017-05-10 Online:2018-01-23 Published:2018-01-24

摘要: 提出一种基于RGBD数据的手势识别方法,首先采用融合深度信息和彩色信息的手势分割算法分割出手势区域;其次提取静态手势轮廓的圆形度、凸包点及凸缺陷点、7Hu矩特征组成特征向量;最后采用SVM进行静态手势识别。实验结果表明,该方法能有效地识别预定义的5种静态手势,且对环境的适应性比较强。

关键词: 手势识别, 深度数据, 手势分割, 特征提取, SVM

Abstract: This paper proposes a hand gesture recognition algorithm based on RGBD data. Firstly, the gesture segmentation algorithm which combines depth data with color data is used to segment the hand gesture area more precisely. Secondly, circularity,convex hull points and convex defect points, 7Hu moment features of the segmented static gestures are extracted. Lastly, SVM are used to recognize different static hand gesture. The experimental results show that the proposed method can effectively identify the five kinds of static gestures, and has strong adaptability to the environment.

Key words: gesture recognition, depth data, gesture segmentation, feature extraction, SVM

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