A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches
Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/441 |
| _version_ | 1797431159137763328 |
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| author | Mohammadreza Javadiha Carlos Andujar Enrique Lacasa |
| author_facet | Mohammadreza Javadiha Carlos Andujar Enrique Lacasa |
| author_sort | Mohammadreza Javadiha |
| collection | DOAJ |
| description | Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature. |
| first_indexed | 2024-03-09T09:40:52Z |
| format | Article |
| id | doaj.art-9027ec9f09a74204ab1843b22b3680fa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9027ec9f09a74204ab1843b22b3680fa2023-12-02T00:56:57ZengMDPI AGSensors1424-82202022-12-0123144110.3390/s23010441A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel MatchesMohammadreza Javadiha0Carlos Andujar1Enrique Lacasa2ViRVIG Research Group, Computer Science Department, Universitat Politècnica de Catalunya-BarcelonaTech, 08034 Barcelona, SpainViRVIG Research Group, Computer Science Department, Universitat Politècnica de Catalunya-BarcelonaTech, 08034 Barcelona, SpainComplex Systems in Sport Research Group, Institut Nacional D’Educacio Fisica de Catalunya (INEFC), University of Lleida (UdL), 25192 Lleida, SpainRecent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.https://www.mdpi.com/1424-8220/23/1/441sports scienceracket sportsvideo-based analysisplayer trackingsport analyticsdata analysis |
| spellingShingle | Mohammadreza Javadiha Carlos Andujar Enrique Lacasa A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches Sensors sports science racket sports video-based analysis player tracking sport analytics data analysis |
| title | A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches |
| title_full | A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches |
| title_fullStr | A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches |
| title_full_unstemmed | A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches |
| title_short | A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches |
| title_sort | query language for exploratory analysis of video based tracking data in padel matches |
| topic | sports science racket sports video-based analysis player tracking sport analytics data analysis |
| url | https://www.mdpi.com/1424-8220/23/1/441 |
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