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基于深度信息的动态手势识别算法

于静1,田国会1*,尹建芹2   

  1. 1.山东大学控制科学与工程学院, 山东 济南 250061;
    2. 济南大学信息科学与工程学院山东省网络环境智能计算技术重点实验室, 山东 济南 250022
  • 收稿日期:2013-12-11 出版日期:2014-06-20 发布日期:2013-12-11
  • 通讯作者: 田国会(1969-),男,河北河涧人,教授,博士生导师,主要研究方向为服务机器人,智能空间和多机器人系统的协调与协作. E-mail:g.h.tian@sdu.edu.cn
  • 作者简介:于静(1989- ),女,山东东营人,硕士研究生,主要研究方向为手势识别和行为理解.E-mail:yjjtcl@163.com
  • 基金资助:
    国家自然科学基金资助项目(61075092)

Dynamic hand gesture recognition algorithm based on depth information

YU Jing1, TIAN Guohui1*, YIN Jianqin2   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China;
    2.  Shandong Provincial Key Laboratory of Network Based Intelligent Computing,
    School of Information Science and Engineering,  University of Jinan, Jinan 250022, Shandong, China
  • Received:2013-12-11 Online:2014-06-20 Published:2013-12-11

摘要: 针对目前手势识别方法计算复杂、特征量提取不可靠等问题,提出基于Kinect传感器深度信息快速动态手势识别算法。通过Kinect的深度摄像头获取深度图像,利用阈值分割法对深度图像进行预处理;结合深度信息,利用OpenCV函数库来提取前景;选用动态时间规整(dynamic time warping)算法计算测试行为模板与参考行为模板之间的相似度以实现样本的分类;最终结合OpenNI和OpenCV,在VS2010环境下实现了该算法。与其他算法相比,该算法改进动态手势特征的提取方法和分类过程,能够快速跟踪手部,有效分割手势。实验结果表明,本方法对具有时空特性的动态手势有很高的识别率,在不同光照和复杂背景下具有较好的鲁棒性。

关键词: 深度信息, 动态时间规整, 手势识别, 行为表示特征量, Kinect

Abstract: To solve the complex calculation and the unreliable feature extraction of gesture recognition, a fast algorithm of dynamic gesture recognition based on the depth information of Kinect was proposed. Firstly,the depth camera of Kinect was used to get the depth image. Then, the method of threshold segmentation was used for image preprocessing, using OpenCV library and depth information to extract the foreground. Finally, Dynamic time warping algorithm calculated the similarity between test behavior template and reference behavior template for classifying the samples. The algorithm was realized under VS2010 by integrating OpenNI and OpenCV. Compared with other algorithms, this algorithm improved the extraction method of the dynamic gesture characteristics and the classification trajectory. Experimental results showed that the proposed method had a high recognition rate for the dynamic hand gestures with characteristics of time and space, and it had robustness under different illumination and complex background.

Key words: depth information, dynamic time warping, behavior representation characteristic, Kinect, gesture recognition

[1] 张冕,黄颖,梅海艺,郭毓. 基于Kinect的配电作业机器人智能人机交互方法[J]. 山东大学学报(工学版), 2018, 48(5): 103-108.
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