Football referee gesture recognition algorithm based on YOLOv8s
Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2024.1341234/full |
| _version_ | 1797303334150864896 |
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| author | Zhiyuan Yang Yuanyuan Shen Yanfei Shen |
| author_facet | Zhiyuan Yang Yuanyuan Shen Yanfei Shen |
| author_sort | Zhiyuan Yang |
| collection | DOAJ |
| description | Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches. |
| first_indexed | 2024-03-07T23:51:18Z |
| format | Article |
| id | doaj.art-9f718bf6c113448498c99589b30234ee |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-03-07T23:51:18Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-9f718bf6c113448498c99589b30234ee2024-02-19T04:56:00ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882024-02-011810.3389/fncom.2024.13412341341234Football referee gesture recognition algorithm based on YOLOv8sZhiyuan YangYuanyuan ShenYanfei ShenGesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.https://www.frontiersin.org/articles/10.3389/fncom.2024.1341234/fullfootball gesture recognitiondeep learningYOLOv8sGAMP2 detection headMDPIoU |
| spellingShingle | Zhiyuan Yang Yuanyuan Shen Yanfei Shen Football referee gesture recognition algorithm based on YOLOv8s Frontiers in Computational Neuroscience football gesture recognition deep learning YOLOv8s GAM P2 detection head MDPIoU |
| title | Football referee gesture recognition algorithm based on YOLOv8s |
| title_full | Football referee gesture recognition algorithm based on YOLOv8s |
| title_fullStr | Football referee gesture recognition algorithm based on YOLOv8s |
| title_full_unstemmed | Football referee gesture recognition algorithm based on YOLOv8s |
| title_short | Football referee gesture recognition algorithm based on YOLOv8s |
| title_sort | football referee gesture recognition algorithm based on yolov8s |
| topic | football gesture recognition deep learning YOLOv8s GAM P2 detection head MDPIoU |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2024.1341234/full |
| work_keys_str_mv | AT zhiyuanyang footballrefereegesturerecognitionalgorithmbasedonyolov8s AT yuanyuanshen footballrefereegesturerecognitionalgorithmbasedonyolov8s AT yanfeishen footballrefereegesturerecognitionalgorithmbasedonyolov8s |