Automated recognition of the cricket batting backlift technique in video footage using deep learning architectures.

Autor: Moodley T; Academy of Computer Science and Software Engineering, University of Johannesburg, Cnr University Road and Kingsway Avenue, Auckland Park Johannesburg, 2092, Gauteng, South Africa., van der Haar D; Academy of Computer Science and Software Engineering, University of Johannesburg, Cnr University Road and Kingsway Avenue, Auckland Park Johannesburg, 2092, Gauteng, South Africa. dvanderhaar@uj.ac.za., Noorbhai H; Biomedical Engineering and Healthcare Technology (BEAHT) Research Centre, Faculty of Health Sciences, University of Johannesburg, Cnr Siemert Beit Street, Doornfontein City Two, 2028, Gauteng, South Africa.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Feb 03; Vol. 12 (1), pp. 1895. Date of Electronic Publication: 2022 Feb 03.
DOI: 10.1038/s41598-022-05966-6
Abstrakt: There have been limited studies demonstrating the validation of batting techniques in cricket using machine learning. This study demonstrates how the batting backlift technique in cricket can be automatically recognised in video footage and compares the performance of popular deep learning architectures, namely, AlexNet, Inception V3, Inception Resnet V2, and Xception. A dataset is created containing the lateral and straight backlift classes and assessed according to standard machine learning metrics. The architectures had similar performance with one false positive in the lateral class and a precision score of 100%, along with a recall score of 95%, and an f1-score of 98% for each architecture, respectively. The AlexNet architecture performed the worst out of the four architectures as it incorrectly classified four images that were supposed to be in the straight class. The architecture that is best suited for the problem domain is the Xception architecture with a loss of 0.03 and 98.2.5% accuracy, thus demonstrating its capability in differentiating between lateral and straight backlifts. This study provides a way forward in the automatic recognition of player patterns and motion capture, making it less challenging for sports scientists, biomechanists and video analysts working in the field.
(© 2022. The Author(s).)
Databáze: MEDLINE
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