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。
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,7 Hu 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.
出处
《计算机与现代化》
2018年第1期74-77,共4页
Computer and Modernization
关键词
手势识别
深度数据
手势分割
特征提取
SVM
gesture recognition
depth data
gesture segmentation
feature extraction
SVM
