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Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm

  • Bei Li
  • , Ying Sun
  • , Gongfa Li*
  • , Jianyi Kong
  • , Guozhang Jiang
  • , Du Jiang
  • , Bo Tao
  • , Shuang Xu
  • , Honghai Liu
  • *Corresponding author for this work
  • Wuhan University of Science and Technology
  • University of Portsmouth

Research output: Contribution to journalArticlepeer-review

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
Pages (from-to)503-512
Number of pages10
JournalCluster Computing
Volume22
DOIs
StatePublished - 16 Jan 2019
Externally publishedYes

Keywords

  • Estimation
  • Gesture recognition
  • Pattern recognition
  • Pursuit algorithm
  • Sparse representation

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