Authors:
Ahmed Nady
1
and
Elsayed E. Hemayed
2
;
3
Affiliations:
1
Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
;
2
Department of Computer Engineering, Faculty of Engineering, Cairo University, Giza 12613, Egypt
;
3
Zewail City of Science and Technology, University of Science and Technology, Giza 12578, Egypt
Keyword(s):
Jersey Number Recognition, Player Identification, Sports Video Analysis.
Abstract:
Identifying players through jersey numbers in sports videos is a challenging task. Jersey number can be distorted and deformed due to variation of the player’s posture and the camera’s view. Moreover, it varies in font and size due to the different sports fields. In this paper, we present a deep learning-based framework to address these challenges of jersey number recognition. Our framework has three main parts. Firstly, it detects players on the court using state of the art object detector YOLO V4. Secondly, each jersey number per detected player bounding boxes is localized. Then a four-stage scene text recognition is employed for recognizing detected number regions. A benchmark dataset consists of three subsets is collected. Two subsets include player images from different fields in basketball sport and the third includes player images from ice hockey sport. Experiments show that the proposed approach is effective compared to state-of-the-art jersey number recognition methods. This
research makes the automation of player identification applicable across several sports.
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