China Mechanical Engineering

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Gesture Recognition Based on Modified Adaptive Orthogonal Matching Pursuit Algorithm

LI Bei1;SUN Ying1,2;LI Gongfa1,2,3;JIANG Guozhang1,2,5;KONG Jianyi1,2;JIANG Du1,4;CHEN Disi1,3   

  1. 1.Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan,430081
    2.Research Center for Biomimetic Robot and Intelligent Measurement and Control,Wuhan University of Science and Technology,Wuhan,430081
    3.Institute of Precision Manufacturing,Wuhan University of Science and Technology,Wuhan,430081
    4.Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan,430081
    5.The Research Institute of 3D Printing and Intelligent Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan,430081
  • Online:2018-07-25 Published:2018-07-27
  • Supported by:
    National Natural Science Foundation of China (No. 51575407,51575338,51575412,61273106)

基于改进自适应正交匹配追踪算法的手势识别

李贝1;孙瑛1,2;李公法1,2,3;蒋国璋1,2,5;孔建益1,2;江都1,4;陈迪斯1,3   

  1. 1.武汉科技大学冶金装备及其控制教育部重点实验室,武汉,430081
    2.武汉科技大学生物机械手与智能测控研究中心,武汉,430081
    3.武汉科技大学精密制造研究院,武汉,430081
    4.武汉科技大学机械传动与制造工程湖北省重点实验室,武汉,430081
    5.武汉科技大学3D打印与智能制造工程研究所,武汉,430081
  • 基金资助:
    国家自然科学基金资助项目(51575407,51575338,51575412,61273106)
    National Natural Science Foundation of China (No. 51575407,51575338,51575412,61273106)

Abstract: A modified adaptive orthogonal matching pursuit(MAOMP) algorithm was proposed to guarantee advantages in sparsity estimation and overcome the disadvantages of increasing fixed step values in sparse solution.The algorithm introduced sparsity and variable step sizes.Initial value of sparsity was estimated by matching tests,and the numbers of subsequent iterations were decreased.Finally,the step sizes were adjusted to select atoms and approximate the true sparsity at different stages.Experimental results show that compared with other greedy algorithms,the proposed algorithm improves the recognition accuracy and efficiency.

Key words: gesture recognition, matching algorithm, greedy algorithm, sparse data, step value

摘要: 针对稀疏求解算法在稀疏度估计上的优势和增加固定步长的不足,提出改进的自适应正交匹配追踪算法。该算法引入稀疏度和变步长步骤,首先通过匹配测试来估计稀疏度初始值,以减少后续迭代次数,然后在不同阶段调整步长来筛选原子数,逼近真实稀疏度。实验结果表明,与其他贪婪算法相比,该算法有效提高了识别精度和效率。

关键词: 手势识别, 匹配算法, 贪婪算法, 稀疏数据, 步长值

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