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. Preprints2023, 2023090648. https://doi.org/10.20944/preprints202309.0648.v1
Veršnik, A.; Šajn, L. Automatic Tracking of NBA Statistics from a Live Broadcast. Preprints 2023, 2023090648. https://doi.org/10.20944/preprints202309.0648.v1
Veršnik, A.; Šajn, L. Automatic Tracking of NBA Statistics from a Live Broadcast. Preprints2023, 2023090648. https://doi.org/10.20944/preprints202309.0648.v1
APA Style
Veršnik, A., & Šajn, L. (2023). Automatic Tracking of NBA Statistics from a Live Broadcast. Preprints. https://doi.org/10.20944/preprints202309.0648.v1
Chicago/Turabian Style
Veršnik, A. and Luka Šajn. 2023 "Automatic Tracking of NBA Statistics from a Live Broadcast" Preprints. https://doi.org/10.20944/preprints202309.0648.v1
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.
Keywords
homography; computer vision; detection; automatic tracking of statistics; basketball; video analysis; neural networks; object tracking
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
