Individual Locating of Soccer Players from a Single Moving View
Positional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7938 |
| _version_ | 1797576958425432064 |
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| author | Adrien Maglo Astrid Orcesi Julien Denize Quoc Cuong Pham |
| author_facet | Adrien Maglo Astrid Orcesi Julien Denize Quoc Cuong Pham |
| author_sort | Adrien Maglo |
| collection | DOAJ |
| description | Positional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder–decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023. |
| first_indexed | 2024-03-10T22:01:13Z |
| format | Article |
| id | doaj.art-38fd7d97e0e94462a08fc43483d8ed64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:13Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-38fd7d97e0e94462a08fc43483d8ed642023-11-19T12:56:26ZengMDPI AGSensors1424-82202023-09-012318793810.3390/s23187938Individual Locating of Soccer Players from a Single Moving ViewAdrien Maglo0Astrid Orcesi1Julien Denize2Quoc Cuong Pham3Université Paris-Saclay, CEA, List, F-91120 Palaiseau, FranceUniversité Paris-Saclay, CEA, List, F-91120 Palaiseau, FranceUniversité Paris-Saclay, CEA, List, F-91120 Palaiseau, FranceUniversité Paris-Saclay, CEA, List, F-91120 Palaiseau, FrancePositional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder–decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023.https://www.mdpi.com/1424-8220/23/18/7938sports field registrationteam sport players trackingneural networkscomputer vision |
| spellingShingle | Adrien Maglo Astrid Orcesi Julien Denize Quoc Cuong Pham Individual Locating of Soccer Players from a Single Moving View Sensors sports field registration team sport players tracking neural networks computer vision |
| title | Individual Locating of Soccer Players from a Single Moving View |
| title_full | Individual Locating of Soccer Players from a Single Moving View |
| title_fullStr | Individual Locating of Soccer Players from a Single Moving View |
| title_full_unstemmed | Individual Locating of Soccer Players from a Single Moving View |
| title_short | Individual Locating of Soccer Players from a Single Moving View |
| title_sort | individual locating of soccer players from a single moving view |
| topic | sports field registration team sport players tracking neural networks computer vision |
| url | https://www.mdpi.com/1424-8220/23/18/7938 |
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