Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction...

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Vydáno v:Wireless communications and mobile computing Ročník 2022; číslo 1
Hlavní autoři: Peng, Yongwei, Gao, Weiyi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Hindawi 2022
John Wiley & Sons, Inc
Témata:
Algorithms
Basketball
Cluster analysis
Clustering
Data mining
Datasets
Efficiency
Experiments
Feature extraction
Feature recognition
Image enhancement
Image processing
Image reconstruction
Image resolution
Image segmentation
Location based services
Machine vision
Methods
Motion capture
Optimization
Roads & highways
Similarity measures
Template matching
Three dimensional models
Training
Trajectory analysis
Vector quantization
ISSN:1530-8669, 1530-8677
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Abstract The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction in detail. By analyzing the trajectory data under the constraints of the road network, the spatiotemporal characteristics of the existing trajectory clustering methods, and the deficiencies of the existing trajectory clustering methods, a new trajectory clustering method based on trajectory segmentation and spatiotemporal similarity measurement is implemented. A motion capture and reconstruction method for basketball training based on visual image K-means clustering algorithm is proposed. Multiresolution frame scanning technology is used to collect machine images of basketball training movements, and edge contour processing is performed on the collected high-resolution basketball training movement images. Feature detection uses the three-dimensional model reconstruction method to segment the basketball training action area and combines the irregular triangle network model to realize the machine vision block template matching processing of basketball training actions and capture the basketball training action in the Gaussian fuzzy affine space. In time and feature extraction, wavelet lifting technology is used to identify the ambiguity of basketball training movements, image enhancement technology is used to improve the resolution and adaptability of basketball training movement capture, and machine vision image processing methods are used to achieve basketball training movement capture optimization. The simulation results show that the method has better adaptability and higher feature recognition ability for basketball training motion capture and improves the feature extraction and adaptive capture reconstruction ability of basketball training motion.
AbstractList The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction in detail. By analyzing the trajectory data under the constraints of the road network, the spatiotemporal characteristics of the existing trajectory clustering methods, and the deficiencies of the existing trajectory clustering methods, a new trajectory clustering method based on trajectory segmentation and spatiotemporal similarity measurement is implemented. A motion capture and reconstruction method for basketball training based on visual image K-means clustering algorithm is proposed. Multiresolution frame scanning technology is used to collect machine images of basketball training movements, and edge contour processing is performed on the collected high-resolution basketball training movement images. Feature detection uses the three-dimensional model reconstruction method to segment the basketball training action area and combines the irregular triangle network model to realize the machine vision block template matching processing of basketball training actions and capture the basketball training action in the Gaussian fuzzy affine space. In time and feature extraction, wavelet lifting technology is used to identify the ambiguity of basketball training movements, image enhancement technology is used to improve the resolution and adaptability of basketball training movement capture, and machine vision image processing methods are used to achieve basketball training movement capture optimization. The simulation results show that the method has better adaptability and higher feature recognition ability for basketball training motion capture and improves the feature extraction and adaptive capture reconstruction ability of basketball training motion.
Author Gao, Weiyi
Peng, Yongwei
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10.1007/s40171-021-00277-7
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Copyright Copyright © 2022 Yongwei Peng and Weiyi Gao.
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SubjectTerms Algorithms
Basketball
Cluster analysis
Clustering
Data mining
Datasets
Efficiency
Experiments
Feature extraction
Feature recognition
Image enhancement
Image processing
Image reconstruction
Image resolution
Image segmentation
Location based services
Machine vision
Methods
Motion capture
Optimization
Roads & highways
Similarity measures
Template matching
Three dimensional models
Training
Trajectory analysis
Vector quantization
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