计算机应用 ›› 2014, Vol. 34 ›› Issue (3): 833-836.DOI: 10.11772/j.issn.1001-9081.2014.03.0833

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于视觉的手势识别方法及其在数字信号处理器上的实现

张毅1,刘钰然1,罗元2   

  1. 1. 重庆邮电大学 自动化学院，重庆400065
    2. 重庆邮电大学 光电工程学院，重庆400065
  • 收稿日期:2013-09-11 修回日期:2013-11-15 发布日期:2014-04-01 出版日期:2014-03-01
  • 通讯作者: 刘钰然
  • 作者简介:张毅(1966-)，男，重庆人，教授，博士，主要研究方向：机器人、数据融合、信息无障碍；刘钰然(1986-)，男，重庆人，硕士研究生，主要研究方向：机器视觉、信息无障碍；罗元(1972-)，女，湖北宜昌人，教授，博士，主要研究方向：机器视觉、智能信号处理、数字图像处理。
  • 基金资助:

    国家科技部国际合作项目

Vision-based gesture recognition method and its implementation on digital signal processor

ZHANG Yi1,LIU Yuran1,LUO Yuan2   

  1. 1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2. College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2013-09-11 Revised:2013-11-15 Online:2014-04-01 Published:2014-03-01
  • Contact: LIU Yuran

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摘要：

针对手势识别算法复杂度高、在嵌入式系统上运行效率低的问题，提出一种以定点运算为主的基于形状特征的手势识别方法。采用内部最大圆法和圆截法提取特征点，在手掌内部寻找一个最大圆来获取掌心坐标；同时根据指尖的几何特征，在手形边缘以画圆的方式获取指尖，从而得到手势的手指数、方向和掌心位置等特征信息；再对这些特征信息进行分类并识别。通过对算法进行改进，完成了在数字信号处理器（DSP）上的移植。实验证明该方法对于不同人的手具有适应性，适合在DSP上处理，与其他基于形状特征的手势识别算法相比，平均识别率提高了1.6%~8.6%，计算机对算法的处理速度提高了2%，因此所提算法有利于嵌入式手势识别系统的实现，为嵌入式手势识别系统打下基础。

关键词: 手势识别, 计算机视觉, 数字信号处理器, 嵌入式系统, 定点运算

Abstract:

The existing gesture recognition algorithms perform inefficiently on the embedded devices for their high complexity. A shape feature-based algorithm with major fixed-point arithmetic was proposed, which used the most significant internal circle algorithm and the circle cutting algorithm to obtain the features. This method could extract the center of a palm by finding the largest circle inside the palm, and could extract the finger tips by drawing circles at the edge of the hand. Finally gestures could be classified and recognized according to the feature information of the number of fingers, orientation and the position of the palm. This algorithm had been transplanted to Digital Signal Processor (DSP) by improving it. The experimental results show that the proposed method can adapt to different hands of different people and it is ideal for DSP. Compared with other shape-based algorithms, the average recognition rate has increased from 1.6%~8.6%, and the speed of the computer processing has increased by 2% by using this algorithm. Therefore, the proposed method facilitates the implementation of embedded gesture recognition systems and lays the foundation for the embedded gesture recognition system.

Key words: Gesture recognition, computer vision, digital signal processor, embedded system, fixed-point arithmetic

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