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Computing Run-out Decisions Using Object Detection and Support Vector Machine Algorithm

Computing Run-out Decisions Using Object Detection and Support Vector Machine Algorithm In the game of cricket, there are various kinds of dismissals that can be caused due to multiple reasons. Few of them include the bowler’s brilliance and the batsman’s mistake. Run-out being one kind of dismissal, that completely changes the fortune and momentum of teams. Most of the time, it becomes difficult for the on-field umpire to give a judgement on run-out with naked eyes. So, the decisions are transferred to the third umpire, who gives the final decision based on a time-consuming technique. Therefore, we are proposing an approach, in which dismissal prediction is made using object detection and support vector machine. As the dismissal prediction is made based on images from different angles from various cameras, we were successful in achieving an accuracy rate of 87%. Additionally, since it works on an automated process, it is much more time-efficient than the traditional system. Thus by using this approach, errors are minimized and machine learning capabilities are provided to decision making in the game of cricket. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Augmented Human Research Springer Journals

Computing Run-out Decisions Using Object Detection and Support Vector Machine Algorithm

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2365-4317
eISSN
2365-4325
DOI
10.1007/s41133-022-00058-6
Publisher site
See Article on Publisher Site

Abstract

In the game of cricket, there are various kinds of dismissals that can be caused due to multiple reasons. Few of them include the bowler’s brilliance and the batsman’s mistake. Run-out being one kind of dismissal, that completely changes the fortune and momentum of teams. Most of the time, it becomes difficult for the on-field umpire to give a judgement on run-out with naked eyes. So, the decisions are transferred to the third umpire, who gives the final decision based on a time-consuming technique. Therefore, we are proposing an approach, in which dismissal prediction is made using object detection and support vector machine. As the dismissal prediction is made based on images from different angles from various cameras, we were successful in achieving an accuracy rate of 87%. Additionally, since it works on an automated process, it is much more time-efficient than the traditional system. Thus by using this approach, errors are minimized and machine learning capabilities are provided to decision making in the game of cricket.

Journal

Augmented Human Research – Springer Journals

Published: Dec 1, 2022

Keywords: Cricket; Machine learning; SVM; Object detection

References

  • The 5th umpire: automating crickets edge detection system
    Rock, R; Als, A; Gibbs, P; Hunte, C
  • Artificial intelligent third umpire runout decision making using image processing
    Nirmala Devi, P