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- W4386618961 abstract "People often make mistakes, so we try to automate every aspect of our lives. Sports is no
 exception. While just over a decade ago humans were analyzing games, today this is being done
 by artificial intelligence. Due to rapid development over the past decade, neural networks are
 now faster, more accurate, and in some areas even better than their human counterparts. In this
 paper, we present an algorithm that can detect player statistics during an NBA broadcast. It
 also helps users better understand the game and the use of augmented reality. The algorithm
 detects players on the court, tracks their movements, and assigns them to their respective teams.
 Using homography estimation, we transform the players’ positions from a three-dimensional space
 in the video to a two-dimensional space on the playing field plane. We define a new algorithm
 that predicts the players’ actions and their statistics. The results show that the proposed method
 can effectively identify the players, their respective teams, and their positions. It can also analyze
 their actions with high accuracy." @default.
- W4386618961 created "2023-09-12" @default.
- W4386618961 creator A5076373653 @default.
- W4386618961 creator A5092849200 @default.
- W4386618961 date "2023-09-11" @default.
- W4386618961 modified "2023-10-18" @default.
- W4386618961 title "Automatic Tracking of NBA Statistics from a Live Broadcast" @default.
- W4386618961 doi "https://doi.org/10.20944/preprints202309.0648.v1" @default.
- W4386618961 hasPublicationYear "2023" @default.
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