Sports video temporal action detection technology based on an improved MSST algorithm
Sports videos contain a large number of irrelevant backgrounds and static frames, which affect the efficiency and accuracy of temporal action detection. To optimize sports video data processing and temporal action detection, an improved multi-level spatiotemporal transformer network model is propose...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-07-01
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| Series: | Nonlinear Engineering |
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| Online Access: | https://doi.org/10.1515/nleng-2025-0143 |
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| author | Lai Lixin Fang Yu |
| author_facet | Lai Lixin Fang Yu |
| author_sort | Lai Lixin |
| collection | DOAJ |
| description | Sports videos contain a large number of irrelevant backgrounds and static frames, which affect the efficiency and accuracy of temporal action detection. To optimize sports video data processing and temporal action detection, an improved multi-level spatiotemporal transformer network model is proposed. The model first optimizes the initial feature extraction of videos through an unsupervised video data preprocessing model based on deep residual networks. Subsequently, multi-scale features are generated through feature pyramid networks. The global spatiotemporal dependencies of actions are captured by a spatiotemporal encoder. The frame-level self-attention module further extracts keyframes and highlights temporal features, thereby improving detection accuracy. The accuracy of the proposed model was 0.6 at the beginning. After 300 iterations, the accuracy was 0.85. After 500 iterations, the highest accuracy was close to 0.9. The mAP of the improved model on the dataset reached 90.5%, which was higher than the 78.2% of the base model. The recall rate was 92.0%, the precision was 89.5%, and the calculation time was 220 ms. Meanwhile, the model shows balanced performance in detecting movements of different types of sports, especially in recognizing complex movements such as gymnastics and diving. This model effectively improves the efficiency and accuracy of time action detection through the collaborative action of multiple modules, demonstrating good applicability and robustness. |
| format | Article |
| id | doaj-art-d709bbf4ccc64c7088d852a1b8fc403f |
| institution | DOAJ |
| issn | 2192-8029 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj-art-d709bbf4ccc64c7088d852a1b8fc403f2025-08-20T03:11:37ZengDe GruyterNonlinear Engineering2192-80292025-07-0114111233310.1515/nleng-2025-0143Sports video temporal action detection technology based on an improved MSST algorithmLai Lixin0Fang Yu1School of Physical Education and Sports, Shenzhen Institute of Information Technology, Shenzhen, 518172, ChinaSchool of Marxism, Shenzhen Institute of Information Technology, Shenzhen, 518172, ChinaSports videos contain a large number of irrelevant backgrounds and static frames, which affect the efficiency and accuracy of temporal action detection. To optimize sports video data processing and temporal action detection, an improved multi-level spatiotemporal transformer network model is proposed. The model first optimizes the initial feature extraction of videos through an unsupervised video data preprocessing model based on deep residual networks. Subsequently, multi-scale features are generated through feature pyramid networks. The global spatiotemporal dependencies of actions are captured by a spatiotemporal encoder. The frame-level self-attention module further extracts keyframes and highlights temporal features, thereby improving detection accuracy. The accuracy of the proposed model was 0.6 at the beginning. After 300 iterations, the accuracy was 0.85. After 500 iterations, the highest accuracy was close to 0.9. The mAP of the improved model on the dataset reached 90.5%, which was higher than the 78.2% of the base model. The recall rate was 92.0%, the precision was 89.5%, and the calculation time was 220 ms. Meanwhile, the model shows balanced performance in detecting movements of different types of sports, especially in recognizing complex movements such as gymnastics and diving. This model effectively improves the efficiency and accuracy of time action detection through the collaborative action of multiple modules, demonstrating good applicability and robustness.https://doi.org/10.1515/nleng-2025-0143sports videostemporal action detectionmultiple time scalesfeature pyramid networkspatiotemporal transformer |
| spellingShingle | Lai Lixin Fang Yu Sports video temporal action detection technology based on an improved MSST algorithm Nonlinear Engineering sports videos temporal action detection multiple time scales feature pyramid network spatiotemporal transformer |
| title | Sports video temporal action detection technology based on an improved MSST algorithm |
| title_full | Sports video temporal action detection technology based on an improved MSST algorithm |
| title_fullStr | Sports video temporal action detection technology based on an improved MSST algorithm |
| title_full_unstemmed | Sports video temporal action detection technology based on an improved MSST algorithm |
| title_short | Sports video temporal action detection technology based on an improved MSST algorithm |
| title_sort | sports video temporal action detection technology based on an improved msst algorithm |
| topic | sports videos temporal action detection multiple time scales feature pyramid network spatiotemporal transformer |
| url | https://doi.org/10.1515/nleng-2025-0143 |
| work_keys_str_mv | AT lailixin sportsvideotemporalactiondetectiontechnologybasedonanimprovedmsstalgorithm AT fangyu sportsvideotemporalactiondetectiontechnologybasedonanimprovedmsstalgorithm |