2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 9 , Issue 2 , PP: 93-107, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Noora Hani Sherif 1 * , Eay Fahidhil 2 , Najlaa Nsrulaah Faris 3 , Hussein Alaa Diame 4 , Raaid Alubady 5 , Seifedine Kadry 6
Doi: https://doi.org/10.54216/JISIoT.090207
Rapid changes in modern technology and sports have impacted society and lifestyle. Augmentative and Alternative Communication (AAC) technology helps to speak and play videos in various sports applications. In the current sports event, AAC's utilization to validate the players' complex moves exclusively has been considered a significant challenge that includes athlete moves in athletics and penalty shots in Soccer. Deep Learning-based Video Segmentation and Video mining (DL-VSVM) with eyeball tracking assistance are proposed to validate the task modeling of sports event video streaming in AAC. The user could select the specific event in the sport and sub-event using eyeball tracking assistance. The AAC is installed with unique icons to identify circumstances. A deep learning-based Sports Task model is created to recognize the required data to be streamed, and the model will help them view the specific sports event they need to watch. The numerical outcomes demonstrate that the suggested DL-VSVM model enhances the segmentation accuracy ratio of 95.3%, tracking ratio of 97.6%, prediction ratio of 98.7%, and reduces the cost function of 5.6% and the error rate of 20.1% compared to other existing models.
Segmentation , deep learning , video streaming Task model and eyeball tracking.
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