An Attention Enhanced Spatial–Temporal Graph Convolutional LSTM Network for Action Recognition in Karate

With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition...

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Vydáno v:Applied sciences Ročník 11; číslo 18; s. 8641
Hlavní autoři: Guo, Jianping, Liu, Hong, Li, Xi, Xu, Dahong, Zhang, Yihan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2021
Témata:
Accuracy
action recognition
Algorithms
Artificial intelligence
attention mechanism
Big Data
College students
Delaunay
Efficiency
karate
Literature reviews
Martial arts
Olympic games
Research methodology
Skills
Sports injuries
Sports training
technical and tactical analysis
China
ISSN:2076-3417, 2076-3417
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Abstract With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition technology, based on deep learning, has gradually been applied to the analysis of the technical actions of competitive sports athletes, as well as the analysis of tactics. In this paper, a new graph convolution model is proposed. Delaunay’s partitioning algorithm was used to construct a new spatiotemporal topology which can effectively obtain the structural information and spatiotemporal features of athletes’ technical actions. At the same time, the attention mechanism was integrated into the model, and different weight coefficients were assigned to the joints, which significantly improved the accuracy of technical action recognition. First, a comparison between the current state-of-the-art methods was undertaken using the general datasets of Kinect and NTU-RGB + D. The performance of the new algorithm model was slightly improved in comparison to the general dataset. Then, the performance of our algorithm was compared with spatial temporal graph convolutional networks (ST-GCN) for the karate technique action dataset. We found that the accuracy of our algorithm was significantly improved.
AbstractList With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition technology, based on deep learning, has gradually been applied to the analysis of the technical actions of competitive sports athletes, as well as the analysis of tactics. In this paper, a new graph convolution model is proposed. Delaunay’s partitioning algorithm was used to construct a new spatiotemporal topology which can effectively obtain the structural information and spatiotemporal features of athletes’ technical actions. At the same time, the attention mechanism was integrated into the model, and different weight coefficients were assigned to the joints, which significantly improved the accuracy of technical action recognition. First, a comparison between the current state-of-the-art methods was undertaken using the general datasets of Kinect and NTU-RGB + D. The performance of the new algorithm model was slightly improved in comparison to the general dataset. Then, the performance of our algorithm was compared with spatial temporal graph convolutional networks (ST-GCN) for the karate technique action dataset. We found that the accuracy of our algorithm was significantly improved.
Author Zhang, Yihan
Liu, Hong
Xu, Dahong
Li, Xi
Guo, Jianping
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ContentType Journal Article
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SubjectTerms Accuracy
action recognition
Algorithms
Artificial intelligence
attention mechanism
Big Data
College students
Delaunay
Efficiency
karate
Literature reviews
Martial arts
Olympic games
Research methodology
Skills
Sports injuries
Sports training
technical and tactical analysis
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