Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm

Bei Li, Ying Sun, Gongfa Li, Jiangyi Kong, Guozhang Jiang, Du Jiang, Bo Tao, Shuang Xu, Honghai Liu

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Abstract

Aiming at the disadvantages of greedy algorithms in sparse solution, a modified adaptive orthogonal matching pursuit algorithm (MAOMP) is proposed in this paper. It is obviously improved to introduce sparsity and variable step size for the MAOMP. The algorithm estimates the initial value of sparsity by matching test, and will decrease the number of subsequent iterations. Finally, the step size is adjusted to select atoms and approximate the true sparsity at different stages. The simulation results show that the algorithm which has proposed improves the recognition accuracy and efficiency comparing with other greedy algorithms.
Original languageEnglish
Number of pages10
JournalCluster Computing
Early online date7 Oct 2017
DOIs
Publication statusEarly online - 7 Oct 2017

Keywords

  • pursuit algorithm
  • gesture recognition
  • pattern recognition
  • sparse representation
  • estimation

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