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Detection of Ice Hockey Players and Teams via a Two-Phase Cascaded CNN Model
Version 1
: Received: 8 May 2020 / Approved: 10 May 2020 / Online: 10 May 2020 (15:00:28 CEST)
How to cite: Guo, T.; Tao, K.; Hu, Q.; Shen, Y. Detection of Ice Hockey Players and Teams via a Two-Phase Cascaded CNN Model. Preprints 2020, 2020050170 Guo, T.; Tao, K.; Hu, Q.; Shen, Y. Detection of Ice Hockey Players and Teams via a Two-Phase Cascaded CNN Model. Preprints 2020, 2020050170
Abstract
Accurate detection of players and teams in ice hockey games is crucial to the tracking of individual players on court and team tactical decisions, which is therefore becoming an important task for coaches and other analysts. However, hockey is a fluid sport due to complex dynamics and frequent substitutions by both teams, resulting in various body positions of players. Few traditional player detection models from other team sports take these characteristics into account, especially for the detection of teams without prior annotations. Here, we design a two-phase cascaded Convolutional Neural Network (CNN) model coupling between individual players position information and team uniform colors to hierarchically detect players in ice hockey games. Our model filters most of disturbing information, such as audience and sideline advertising bars, in Phase I, and refines the detection of targeted players in Phase II, which is efficient and accurate with a precision rate of 91.30% and a recall rate of 95.60% for individual players detection, and an average accuracy of 93.05% in team classification from a self-built dataset of collected images in the 2018 Winter Olympics. Meanwhile, we also present results based on the images and real-time detection from broadcasting videos of 2019-20 NHL regular games covering all 31 teams to show the robustness of our model.
Keywords
player detection; team detection; player tracking data; ice hockey
Subject
Computer Science and Mathematics, Computer Science
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.
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