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Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition

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  • Zheming Zuo
  • Bo Wei
  • Fei Chao
  • Yanpeng Qu
  • Yonghong Peng
  • Longzhi Yang
Article number7
<mark>Journal publication date</mark>31/03/2019
<mark>Journal</mark>Applied System Innovation
Issue number1
Number of pages14
Publication StatusPublished
Early online date19/02/19
<mark>Original language</mark>Undefined/Unknown


Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%