Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash
In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4550 |
| _version_ | 1797527864724160512 |
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| author | Christopher Brumann Markus Kukuk Claus Reinsberger |
| author_facet | Christopher Brumann Markus Kukuk Claus Reinsberger |
| author_sort | Christopher Brumann |
| collection | DOAJ |
| description | In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player’s feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario. |
| first_indexed | 2024-03-10T09:49:52Z |
| format | Article |
| id | doaj.art-761daedae2a949a68c85c13d48aaedce |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:49:52Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-761daedae2a949a68c85c13d48aaedce2023-11-22T02:50:59ZengMDPI AGSensors1424-82202021-07-012113455010.3390/s21134550Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in SquashChristopher Brumann0Markus Kukuk1Claus Reinsberger2Department of Computer Science, University of Applied Sciences and Arts Dortmund, 44139 Dortmund, GermanyDepartment of Computer Science, University of Applied Sciences and Arts Dortmund, 44139 Dortmund, GermanyPaderborn University, Department of Exercise and Health, Institute of Sports Medicine, 33098 Paderborn, GermanyIn sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player’s feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario.https://www.mdpi.com/1424-8220/21/13/4550racket sportssports analysisvideo trackinghuman pose estimation |
| spellingShingle | Christopher Brumann Markus Kukuk Claus Reinsberger Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash Sensors racket sports sports analysis video tracking human pose estimation |
| title | Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash |
| title_full | Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash |
| title_fullStr | Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash |
| title_full_unstemmed | Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash |
| title_short | Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash |
| title_sort | evaluation of open source and pre trained deep convolutional neural networks suitable for player detection and motion analysis in squash |
| topic | racket sports sports analysis video tracking human pose estimation |
| url | https://www.mdpi.com/1424-8220/21/13/4550 |
| work_keys_str_mv | AT christopherbrumann evaluationofopensourceandpretraineddeepconvolutionalneuralnetworkssuitableforplayerdetectionandmotionanalysisinsquash AT markuskukuk evaluationofopensourceandpretraineddeepconvolutionalneuralnetworkssuitableforplayerdetectionandmotionanalysisinsquash AT clausreinsberger evaluationofopensourceandpretraineddeepconvolutionalneuralnetworkssuitableforplayerdetectionandmotionanalysisinsquash |