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Feature covariance matrix-based dynamic hand gesture recognition

  • Linpu Fang
  • Guile Wu
  • Wenxiong Kang
  • Qiuxia Wu
  • Zhiyong Wang
  • David Dagan Feng
Original Article
  • 26 Downloads

Abstract

Over the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible solutions for HGR, it is still very challenging to satisfy the requirements of both high efficiency and accuracy for real-world HGR systems. In this paper, we propose a novel method using the feature covariance matrix for effective and efficient dynamic HGR. We extract a set of local feature vectors that represent local motion patterns to construct the feature covariance matrix efficiently, which also provides a compact representation of a dynamic hand gesture. By tracking hand keypoints in three successive frames and calculating their motion features, our method can be extended to both 2D dynamic HGR and 3D dynamic HGR. To evaluate the effectiveness of the proposed framework, we perform extensive experiments on three publicly available datasets (one 2D dataset and two 3D datasets). The experimental results demonstrate the effectiveness of our proposed method.

Keywords

Dynamic hand gesture recognition Feature covariance matrix Pyramid Lucas–Kanade tracker Temporal hierarchical construction 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61573151), the Guangdong Natural Science Foundation (No. 2016A030313468) and Science and Technology Planning Project of Guangdong Province (No. 2017A010101026).

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.School of Information TechnologiesThe University of SydneyCamperdownAustralia

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