Hidden Markov Model-Based Video Recognition for Sports
In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and pe...
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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: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2021/5183088 |
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| _version_ | 1832551642076020736 |
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| author | Zhiyuan Wang Chongyuan Bi Songhui You Junjie Yao |
| author_facet | Zhiyuan Wang Chongyuan Bi Songhui You Junjie Yao |
| author_sort | Zhiyuan Wang |
| collection | DOAJ |
| description | In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that require real-time feedback and some public indoor scenarios, which can achieve different prevention and services for different age groups. With the increase of 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 to extract features from the video in time and space. Multiple binary classifications of the extracted features are performed to achieve the goal of multilabel classification. A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively. |
| format | Article |
| id | doaj-art-e6dcef294c284d6f813b0ff095e6b8d1 |
| institution | Kabale University |
| issn | 1687-9139 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-e6dcef294c284d6f813b0ff095e6b8d12025-02-03T06:01:00ZengWileyAdvances in Mathematical Physics1687-91392021-01-01202110.1155/2021/5183088Hidden Markov Model-Based Video Recognition for SportsZhiyuan Wang0Chongyuan Bi1Songhui You2Junjie Yao3International College of FootballInternational College of FootballInternational College of FootballSchool of Computer Science and TechnologyIn this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that require real-time feedback and some public indoor scenarios, which can achieve different prevention and services for different age groups. With the increase of 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 to extract features from the video in time and space. Multiple binary classifications of the extracted features are performed to achieve the goal of multilabel classification. A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively.http://dx.doi.org/10.1155/2021/5183088 |
| spellingShingle | Zhiyuan Wang Chongyuan Bi Songhui You Junjie Yao Hidden Markov Model-Based Video Recognition for Sports Advances in Mathematical Physics |
| title | Hidden Markov Model-Based Video Recognition for Sports |
| title_full | Hidden Markov Model-Based Video Recognition for Sports |
| title_fullStr | Hidden Markov Model-Based Video Recognition for Sports |
| title_full_unstemmed | Hidden Markov Model-Based Video Recognition for Sports |
| title_short | Hidden Markov Model-Based Video Recognition for Sports |
| title_sort | hidden markov model based video recognition for sports |
| url | http://dx.doi.org/10.1155/2021/5183088 |
| work_keys_str_mv | AT zhiyuanwang hiddenmarkovmodelbasedvideorecognitionforsports AT chongyuanbi hiddenmarkovmodelbasedvideorecognitionforsports AT songhuiyou hiddenmarkovmodelbasedvideorecognitionforsports AT junjieyao hiddenmarkovmodelbasedvideorecognitionforsports |