Alsaif Dataset Creation and Evaluation for Gymnastics Movement
DOI:
https://doi.org/10.59992/IJCI.2024.v3n6p1الكلمات المفتاحية:
Gymnastics Sports، Creation Dataset، Computer vision، Deep Learingالملخص
Computer vision systems play a major role in many areas of life, as reliance has clearly begun on these systems in the medical field, where they help doctors diagnose diseases correctly. In addition, they are relied upon in the sports field, where they can help referees in giving correct arbitration decisions. The accuracy of computer vision systems has increased with the emergence of deep learning techniques, which require a large amount of data on which a deep learning model can be trained. Therefore, if any deep learning model is relied upon to detect and distinguish any movement or any entity within images or video clips, it must first Training this model on a set of images of these movements or objects to be discovered and distinguished. From this standpoint, this article presents the construction of a database for one of the important games, the gymnastics game, which is popular in many countries. A database has been built for the gymnastics game, which can be used in the application. Deep learning models were developed in order to help coaches and referees in this game, and for these movements there is no database available on the Internet. A group of video clips spread on the Internet was relied upon to build this base, where ten basic movements in the game of gymnastics were distinguished, and one of the models was evaluated. Deep learning on these images (yolo7).
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