Human skeleton pose and spatio-temporal feature-based activity recognition using ST-GCN

Skeleton-based Human Activity Recognition has recently sparked a lot of attention because skeleton data has proven resistant to changes in lighting, body sizes, dynamic camera perspectives, and complicated backgrounds. The Spatial-Temporal Graph Convolutional Networks (ST-GCN) model has been exposed...

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Vydáno v:Multimedia tools and applications Ročník 83; číslo 5; s. 12705 - 12730
Hlavní autoři: Lovanshi, Mayank, Tiwari, Vivek
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2024
Springer Nature B.V
Témata:
Algorithms
Artificial neural networks
Comparative studies
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Deep learning
Feature recognition
Human activity recognition
Human body
Human mechanics
Human motion
Information technology
Kinetics
Model accuracy
Multimedia
Multimedia Information Systems
Neural networks
Pose estimation
Sentiment analysis
Smart houses
Special Purpose and Application-Based Systems
Surveillance
Topology
Activity recognition
ST-GCN
Skeleton joints
Spatio-temporal feature
Pose estimation
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:Skeleton-based Human Activity Recognition has recently sparked a lot of attention because skeleton data has proven resistant to changes in lighting, body sizes, dynamic camera perspectives, and complicated backgrounds. The Spatial-Temporal Graph Convolutional Networks (ST-GCN) model has been exposed to study spatial and temporal dependencies effectively from skeleton data. However, efficient use of 3D skeleton in-depth information remains a significant challenge, specifically for human joint motion patterns and linkages information. This study attempts a promising solution through a custom ST-GCN model and skeleton joints for human activity recognition. Special attention was given to spatial & temporal features, which were further fed to the classification model for better pose estimation. A comparative study is presented for activity recognition using large-scale databases such as NTU-RGB-D, Kinetics-Skeleton, and Florence 3D datasets. The Custom ST-GCN model outperforms (Top-1 accuracy) the state-of-the-art method on NTU-RGB-D, Kinetics-Skeleton & Florence 3D dataset with a higher margin by 0.7%, 1.25%, and 1.92%, respectively. Similarly, with Top-5 accuracy, the Custom ST-GCN model offers results hike by 0.5%, 0.73% & 1.52%, respectively. It shows that the presented graph-based topologies capture the changing aspects of a motion-based skeleton sequence better than some of the other approaches.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16001-9