Authors:
Jakub Eichner
1
;
Jan Nowak
1
;
2
;
Bartłomiej Grzelak
1
;
3
;
Tomasz Górecki
1
;
Tomasz Piłka
1
and
Krzysztof Dyczkowski
1
Affiliations:
1
Adam Mickiewicz University, Poznań, Poland
;
2
Poznan Supercomputing and Networking Center, Poznań, Poland
;
3
KKS Lech Poznań, Poznań, Poland
Keyword(s):
Computer Vision in Football, Object Detection and Tracking, Team Classification in Sports, Deep Learning for Sports Analytics, Football Match Analysis.
Abstract:
This paper introduces an integrated pipeline for detecting, classifying, and tracking key objects within soccer match footage. Our research uses datasets from KKS Lech Poznań, SoccerDB, and SoccerNet, considering various stadium environments and technical conditions, such as equipment quality and recording clarity. These factors mirror the real-world scenarios encountered in competitions, training sessions, and observations. We assessed the effectiveness of cutting-edge object detection models, focusing on several R-CNN frameworks and the YOLOv8 methodology. Additionally, for assigning players to their respective teams, we compared the performance of the K-means algorithm with that of the Multi-Modal Vision Transformer CogVLM model. Despite challenges like suboptimal video resolution and fluctuating weather conditions, our proposed solutions have successfully demonstrated high precision in detecting and classifying key elements such as players and the ball within soccer match footage
. These findings establish a robust basis for further video analysis in soccer, which could enhance tactical strategies and the automation of match summarization.
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