A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Yuan-guo, Liu; Wu, Ying

doi:10.18280/ts.380307

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The performance of the data fusion algorithm directly affects the accuracy of posture recognition and the reliability of the system [196,197]. Data fusion strategies include multi-sensor-based data fusion [17,198,199], position and posture-based fusion [200], multifeature fusion [201,202], and so on.…”

Section: Datasetsmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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Section: Datasetsmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…In recent years, the number of sports videos has exploded thanks to the development of Internet technology and multimedia technology [1][2][3][4]. To acquire valuable information about players, actions, events from sports videos, it is necessary to analyze sports videos and summarize the tactics [5][6][7][8][9]. Compared with that of other sports, the video analysis of basketball is rather complicated, because basketball requires the cooperation between multiple players.…”

Section: Introductionmentioning

confidence: 99%

Visual Image Recognition of Basketball Turning and Dribbling Based on Feature Extraction

Zhang¹,

Wang²

2022

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The processing of basketball videos with complex contents faces several challenges in terms of global motion features, group motion features, and individual pose features. The current research cannot solve problems, such as the diverse spatiotemporal features of actions, the utilization of correspondence between spatiotemporal features, the increase of data volume, and the complexity of the network. To solve these problems, this paper studies the visual image recognition of basketball turning and dribbling based on feature extraction. Specifically, the optical flow image was introduced to establish the relationship between the velocity field of the basketball turning and dribbling and the grayscale of the image frame, such as to effectively depict the time variation of pixels. In addition, a convolutional neural network was established based on multi-feature learning to process the sports video image frames, and to extract more spatiotemporal features of basketball turning and dribbling. To improve the feature utilization of the action recognition model, this paper strengthens the extraction of dynamic and static features for the recognition of the player's basketball turning and dribbling in the same scene, and improves the existing convolutional neural network. Furthermore, the multi-feature learning of motion excitation and temporal aggregation of actions were completed. The proposed model was proved effective through experiments.

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Intelligent Training Assistance System for Sports Posture Correction Feedback Based on Artificial Intelligence Algorithms

2024

2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)

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A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Cited by 3 publications

References 21 publications

A Survey on Artificial Intelligence in Posture Recognition

A Survey on Artificial Intelligence in Posture Recognition

Visual Image Recognition of Basketball Turning and Dribbling Based on Feature Extraction

Intelligent Training Assistance System for Sports Posture Correction Feedback Based on Artificial Intelligence Algorithms

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