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DC Field | Value | Language |
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dc.contributor.author | Devanandan, M. | - |
dc.contributor.author | Rasaratnam, V. | - |
dc.contributor.author | Anbalagan, M.K. | - |
dc.contributor.author | Asokan, N. | - |
dc.contributor.author | Panchendrarajan, R. | - |
dc.contributor.author | Tharmaseelan, J. | - |
dc.date.accessioned | 2022-02-07T10:10:12Z | - |
dc.date.available | 2022-02-07T10:10:12Z | - |
dc.date.issued | 2021-12-09 | - |
dc.identifier.issn | 978-1-6654-0862-2/21 | - |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/1000 | - |
dc.description.abstract | Cricket is one of the top 10 most played sport across the world regardless of age and gender. However, learning cricket has been quite challenging as the majority of the cricket-playing individuals are unable to afford quality infrastructure. While this has opened up many research opportunities to provide solutions to automatically learn cricket, very little work has been done in this era. In this paper, we focus on the batting skills of cricket players. We develop a Random Forest model to classify the cricket shot images using human body keypoints extracted with MediaPipe. Experiment results show the proposed model achieves an F1-score of 87% and outperforms the existing solution in a 5% margin. Further, we propose a similarity estimation approach to compare the user’s cricket image with popular international cricket players’ cricket shot images of the same type and retrieve the most similar one. The mobile application we developed based on our solution will enable cricket-playing individuals to analyze, improve and track their batting performances without the need of having a coach. | en_US |
dc.description.sponsorship | Co-Sponsor:Institute of Electrical and Electronic Engineers (IEEE) Academic sponsor:SLIIT UNI Gold Sponsor :London Stock Exchange Group (LSEG) | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT | en_US |
dc.subject | Cricket Shot | en_US |
dc.subject | Image Classification | en_US |
dc.subject | Decision Tree | en_US |
dc.subject | Random Forest Algorithm | en_US |
dc.subject | MediaPipe | en_US |
dc.title | Cricket Shot Image Classification Using Random Forest | en_US |
dc.type | Article | en_US |
dc.identifier.doi | DOI: 10.1109/ICAC54203.2021.9671109 | en_US |
Appears in Collections: | 3rd International Conference on Advancements in Computing (ICAC) | 2021 Department of Computer systems Engineering-Scopes |
Files in This Item:
File | Description | Size | Format | |
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Cricket_Shot_Image_Classification_Using_Random_Forest.pdf Until 2050-12-31 | 11.94 MB | Adobe PDF | View/Open Request a copy |
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