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Analysis of sports video using image recognition of sportsmen

Author

Listed:
  • Long Wang

    (Northwestern Polytechnical University, Sports Department)

  • Ashutosh Sharma

    (Southern Federal University)

Abstract

The aim of the paper is to meet the needs of different audiences for sports video recognition and provide a reference for sports video image recognition, this paper takes player number recognition as an example and proposes a player number recognition method. Due to the different printing patterns of character numbers on each team's uniform, it is difficult to locate the changes of font, size and direction, and these characters and numbers have non-rigid deformation. To solve the problem that OCR software is difficult to recognize directly, the character number is segmented by the image segmentation method based on edge detection, and the training samples are constructed based on the affine transformation of the image. The results show that: finally, k-nearest neighbor algorithm is used to recognize the segmented character number. After the simulation test, the accuracy rate of this method is 91%, and the single character recognition time is 0.05 s. The conclusion shows that when a character is wrongly divided into another character, we can correct it by the context information of the characters before and after.

Suggested Citation

  • Long Wang & Ashutosh Sharma, 2022. "Analysis of sports video using image recognition of sportsmen," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 557-563, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01539-4
    DOI: 10.1007/s13198-021-01539-4
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