Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Automatic Tracking of NBA Statistics from a Live Broadcast

Version 1 : Received: 8 September 2023 / Approved: 11 September 2023 / Online: 11 September 2023 (10:04:43 CEST)

How to cite: Veršnik, A.; Šajn, L. Automatic Tracking of NBA Statistics from a Live Broadcast. Preprints 2023, 2023090648. Veršnik, A.; Šajn, L. Automatic Tracking of NBA Statistics from a Live Broadcast. Preprints 2023, 2023090648.


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.


homography; computer vision; detection; automatic tracking of statistics; basketball; video analysis; neural networks; object tracking


Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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