Human Action Recognition in Unconstrained Trimmed Videos Using Residual Attention Network and Joints Path Signature

Human Action Recognition in Unconstrained Trimmed Videos Using Residual Attention Network and Joints Path Signature

Action recognition has been achieved great progress in recent years because of better feature representation learning and classification technology like convolutional neural networks (CNNs). However, most current deep learning approaches treat the action recognition as a black box, ignoring the spec...

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Bibliographic Details
Main Authors: Tasweer Ahmad, Lianwen Jin, Jialuo Feng, Guozhi Tang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Convolutional neural networks
residual-attention
path signature features
Online Access:https://ieeexplore.ieee.org/document/8812659/

Internet

https://ieeexplore.ieee.org/document/8812659/

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