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

Zuo, Zheming and Wei, Bo and Chao, Fei and Qu, Yanpeng and Peng, Yonghong and Yang, Longzhi (2019) Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition. Applied System Innovation, 2 (1): 7.

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Abstract

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%

Item Type:
Journal Article
Journal or Publication Title:
Applied System Innovation
Subjects:
?? saliency maplocal feature descriptorsegocentril action recognitionhoghmghofmbh ??
ID Code:
171671
Deposited By:
Deposited On:
14 Jun 2022 08:25
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 22:43