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

Tevin Moodley,Dustin Van Der Haar,Habib Noorbhai

doi:10.1038/s41598-022-05966-6

Tevin Moodley, Dustin Van Der Haar + Show 1 more

Open Access

https://doi.org/10.1038/s41598-022-05966-6

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Journal: Scientific Reports	Publication Date: Feb 3, 2022
Citations: 5	License type: open-access

Affiliation: University of Johannesburg

Abstract

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.

Highlights

There have been limited studies demonstrating the validation of batting techniques in cricket using machine learning
The cricket batting technique is intricate that involves a series of complex gestures needed to perform a stroke, one of these gestures performed by the batsman is referred to as the batting backlift technique (BBT)[14]
There are two backlifts investigated in this study, namely the lateral batting backlift technique (LBBT), and the straight batting backlift technique (SBBT)

Summary

Introduction

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. 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, demonstrating its capability in differentiating between lateral and straight backlifts. Mobile applications have been developed to analyse team performance, player injury, and match prediction[3,10–12]. While these applications have made several improvements within the cricketing domain, there is a lack of research dedicated toward the enhancement and improvement of cricket batting[13]. The SBBT is represented whenever the toe and face of the bat are pointed toward the stumps and g round[13]

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