Player Identification in Different Sports

Ahmed Nady, Elsayed E. Hemayed

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

External IDs:dblp:conf/visapp/NadyH21