scholarly journals Human Action Recognition based on Spatio-temporal Feature Extraction using Pre-trained CNN Model and LSTM approach

2019 ◽  
Vol 7 (2) ◽  
pp. 238-242
Author(s):  
J. Jerusha ◽  
S. Aravind Kumar
Keyword(s):  
Feature Extraction ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Spatio Temporal ◽  
Temporal Feature

Human Action Recognition Using Spatio-Temporal Multiplier Network and Attentive Correlated Temporal Feature

2021 ◽  
Author(s):  
C. Indhumathi ◽  
V. Murugan ◽  
G. Muthulakshmii
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Human Action Recognition ◽  
Human Action ◽  
Regional Correlation ◽  
Temporal Features ◽  
Adaptive Motion ◽  
Spatio Temporal ◽  
Inter Frame ◽  
Temporal Feature

Nowadays, action recognition has gained more attention from the computer vision community. Normally for recognizing human actions, spatial and temporal features are extracted. Two-stream convolutional neural network is used commonly for human action recognition in videos. In this paper, Adaptive motion Attentive Correlated Temporal Feature (ACTF) is used for temporal feature extractor. The temporal average pooling in inter-frame is used for extracting the inter-frame regional correlation feature and mean feature. This proposed method has better accuracy of 96.9% for UCF101 and 74.6% for HMDB51 datasets, respectively, which are higher than the other state-of-the-art methods.

Spatio-temporal SRU with global context-aware attention for 3D human action recognition

2020 ◽  
Vol 79 (17-18) ◽  
pp. 12349-12371
Author(s):  
Qingshan She ◽  
Gaoyuan Mu ◽  
Haitao Gan ◽  
Yingle Fan
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Context Aware ◽  
Global Context ◽  
Spatio Temporal
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