Gesture Recognition Based on Modified Adaptive Orthogonal Matching Pursuit Algorithm

被引：0
作者：
Li B. [1 ]
Sun Y. [1 ,2 ]
Li G. [1 ,2 ]
Jiang G. [1 ,2 ,5 ]
Kong J. [1 ,2 ]
Jiang D. [1 ,4 ]
Chen D. [1 ,3 ]
机构：
[1] Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan
[2] Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan
[3] Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan
[4] Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan
[5] The Research Institute of 3D Printing and Intelligent Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan
来源：
Li, Gongfa (ligongfa@wust.edu.cn) | 1736年 / Chinese Mechanical Engineering Society卷 / 29期
关键词：
Gesture recognition; Greedy algorithm; Matching algorithm; Sparse data; Step value;
D O I：
10.3969/j.issn.1004-132X.2018.14.014
中图分类号：
学科分类号：
摘要：
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. © 2018, China Mechanical Engineering Magazine Office. All right reserved.
引用
收藏
页码：1736 / 1742
页数：6
相关论文
共 19 条
  • [1] Guan R., Xu X., Luo Y., Et al., A Computer Vision-based Gesture Detection and Recognition Technique, Computer Applications and Software, 30, 1, pp. 155-159, (2013)
  • [2] Yi J., Cheng J., Ku X., Review of Gestures Recognition Based on Vision, Computer Science, 43, pp. 103-108, (2016)
  • [3] Li X., Wu J., Cui Z., Et al., Sparse Representation Method of Vehicle Recognition in Complex Traffic Scenes, Journal of Image and Graphics, 17, 3, pp. 90-95, (2012)
  • [4] Cui M., Prasad S., Class-dependent Sparse Representation Classifier for Robust Hyperspectral Image Classification, IEEE Transactions on Geoscience & Remote Sensing, 53, 5, pp. 2683-2695, (2015)
  • [5] Wright J., Yang A.Y., Ganesh A., Et al., Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 2, pp. 210-227, (2009)
  • [6] Meng F.R., Tang Z.Y., Wang Z.X., An Improved Redundant Dictionary Based on Sparse Representation for Face Recognition, Multimedia Tools and Applications, 76, 1, pp. 895-912, (2017)
  • [7] Ning Y., Li D., Han X., Et al., Gesture Recognition Method Based on Sparse Representation, Computer Engineering and Design, 37, 9, pp. 2548-2552, (2016)
  • [8] Guo Y.M., Zhao G.Y., Pietikainen M., Dynamic Facial Expression Recognition with Atlas Construction and Sparse Representation, IEEE Transactions on Image Processing, 25, 5, pp. 1977-1992, (2016)
  • [9] Miao W., Li G.F., Sun Y., Et al., Gesture Recognition Based on Sparse Representation, Int.J.Wireless and Mobile Computing, 11, 4, pp. 348-356, (2016)
  • [10] Yang W.J., Kong L.F., Wang M.Y., Hand Gesture Recognition Using Saliency and Histogram Intersection Kernel Based Sparse Representation, Multimedia Tools and Applications, 75, 10, pp. 6021-6034, (2016)
12