Sports Navigator – A Framework for all Sports Intelligence using Machine Learning (Game Census
Srinath R1, NagaSwetha Devarakonda2, Arun Biradar3
1Srinath R*, Associate Professor, Dept of ISE, NIE, Mysuru, India.
2Naga Swetha Devarakonda Computer Network Engineering from VTU, Belegavi at NIE, Mysuru. India.
3Arun Biradar, Professor & Head, Dept of Computer Science & engg, EWIT, Bengaluru, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 293-296 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6190129219/2019©BEIESP | DOI: 10.35940/ijitee.B6190.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are significant advances in the field of Machine Learning and Data analytics over the past few years and its use-cases has spread across various application areas starting from Transportation to Medical. Sports industry has been recently reaping the benefit of Machine Learning. People are looking to improve their games from all avenues. There is also a significant increase in the Interest in games among parents and kids. However, there is no one platform which creates a digital footprint of each player so a player can be tracked from his early participation in games till he starts playing professional games. We propose a platform which will collect data from all the videos played by players and create a digital footprint of each of the players and games. This paper particularly covers a game census with comparison between attacking team and opponent team.
Keywords: Game census, Machine Learning, Tagging.
Scope of the Article: Machine Learning