Semi-automatic player detection for real-time broadcasting systems

Kang, Min-Sung; Lee, J. W.; Kang, Sangcheol; Lim, Young-Chul

doi:10.1109/icce.2017.7889310

Cited by 2 publications

(2 citation statements)

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“…Automatic annotation of broadcast sport videos is an interesting area for researchers. Some approaches deal with ball and field detection [1], and many papers focus on player detection and tracking [2,3,4,7,10]. The approach in [2] is a semi automatic approach for tracking and detecting an important player in a shot.…”

Section: Related Workmentioning

confidence: 99%

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Video annotation with metadata: A case study of soccer game video annotation with players

2022

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In recent years, the use of convolutional neural networks for video processing has become very attractive. The reason lies in the computational power for data processing which is available today. There are many well-defined research areas where neural networks have brought higher reliability than other conventional approaches; for example, traffic sign recognition and isolated number recognition. In this paper, we will describe the architecture and the implementation of the process of soccer game annotation. The game is annotated with data about players. The technology of convolutional neural networks is used for number recognition. The process runs in real-time on a streaming video. Content enriched with metadata is given to the user in parallel with the real-time video. In the paper, we will describe in some detail the following modules: Image binarization, shot localization, the selection and recognition of numbers on players` jerseys.

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Section: Related Workmentioning

confidence: 99%

“…Some approaches deal with ball and field detection [1], and many papers focus on player detection and tracking [2,3,4,7,10]. The approach in [2] is a semi automatic approach for tracking and detecting an important player in a shot. It requires operator assistance, which adds time delay.…”

Section: Related Workmentioning

confidence: 99%