Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence
In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5515357 |
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| _version_ | 1849304731507228672 |
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| author | Zhao Zhang Wang Li Yuyang Zhang |
| author_facet | Zhao Zhang Wang Li Yuyang Zhang |
| author_sort | Zhao Zhang |
| collection | DOAJ |
| description | In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications. |
| format | Article |
| id | doaj-art-3dad8fd40ae84462bd6c1b2b7e5ee04f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3dad8fd40ae84462bd6c1b2b7e5ee04f2025-08-20T03:55:40ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55153575515357Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial IntelligenceZhao Zhang0Wang Li1Yuyang Zhang2Sichuan Normal University, Chengdu, Sichuan 610066, ChinaSichuan Tourism University, Chengdu, Sichuan 610100, ChinaSichuan Nursing Vocational College, Chengdu, Sichuan 610100, ChinaIn this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.http://dx.doi.org/10.1155/2021/5515357 |
| spellingShingle | Zhao Zhang Wang Li Yuyang Zhang Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence Complexity |
| title | Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence |
| title_full | Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence |
| title_fullStr | Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence |
| title_full_unstemmed | Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence |
| title_short | Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence |
| title_sort | automatic construction and extraction of sports moment feature variables using artificial intelligence |
| url | http://dx.doi.org/10.1155/2021/5515357 |
| work_keys_str_mv | AT zhaozhang automaticconstructionandextractionofsportsmomentfeaturevariablesusingartificialintelligence AT wangli automaticconstructionandextractionofsportsmomentfeaturevariablesusingartificialintelligence AT yuyangzhang automaticconstructionandextractionofsportsmomentfeaturevariablesusingartificialintelligence |