Robust Basketball Player Tracking Based on a Hybrid Detection Grouping Framework for Overlapping Cameras

Wu, Kuan-Hsien; Tsai, Wen-Hsuan; Pan, Tse-Yu; Hu, Min‐Chun

doi:10.1109/bigdata47090.2019.9005551

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…And in the last step, we determine the action they perform. There are a variety of works that deal with one or more of these steps [4][5][6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

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Automatic Tracking of NBA Statistics from a Live Broadcast

Veršnik,

Šajn

2023

Preprint

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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.

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“…And in the last step, we determine the action they perform. There are a variety of works that deal with one or more of these steps [4][5][6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

“…Player detection can be done in many different ways. We can use a HOG detector [5,6], a deformable part model [4], a particle filter model for tracking moving targets [7], the AlphaPose algorithm [9], or a neural network such as YOLO [10]. Teams are primarily classified based on the color of their jerseys [4][5][6].…”

Section: Related Workmentioning

confidence: 99%