Learning Long-Term Temporal Features With Deep Neural Networks for Human Action Recognition

Learning Long-Term Temporal Features With Deep Neural Networks for Human Action Recognition

One of challenging tasks in the field of artificial intelligence is the human action recognition. In this paper, we propose a novel long-term temporal feature learning architecture for recognizing human action in video, named Pseudo Recurrent Residual Neural Networks (P-RRNNs), which exploits the re...

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Bibliographic Details
Main Authors: Sheng Yu, Li Xie, Lin Liu, Daoxun Xia
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Action recognition
residual learning
recurrent neural networks
long short-term memory (LSTM)
Online Access:https://ieeexplore.ieee.org/document/8943218/

Internet

https://ieeexplore.ieee.org/document/8943218/

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