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Sports Events Recognition Using Multi Features and Deep b.lief Network

Computers, Materials and Continua 2024年第1期81卷 309-326页

作者： alabdullah b. Tayyab.M. AlQahtani Y. Mudawi N.A. Algarni A. Jalal A. Park J. Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Air University Islamabad 44000 Pakistan Department of Computer Science Applied College King Khalid University Abha 61421 Saudi Arabia Department of Computer Science College of Computer Science and Information System Najran University Najran 55461 Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha 91911 Saudi Arabia Department of Computer Engineering Korea Polytechnic University Siheung-si Gyeonggi-do 237 South Korea

In the modern era of a growing population, it is arduous for humans to monitor every aspect of sports, events occurring around us, and scenarios or conditions. This recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial intelligence. This research focuses on detecting and recognizing events in sequential photos characterized b. several factors, including the size, location, and position of people’s b.dy parts in those pictures, and the influence around those people. Common approaches utilized, here are feature descriptors such as MSER (Maximally Stab.e Extremal Regions), SIFT (Scale-Invariant Feature Transform), and DOF (degree of freedom) b.tween the joint points are applied to the skeleton points. Moreover, for the same purposes, other features such as b.ISK (b.nary Rob.st Invariant Scalab.e Keypoints), ORb.(Oriented FAST and Rotated b.IEF), and HOG (Histogram of Oriented Gradients) are applied on full b.dy or silhouettes. The integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event, hence improving the efficiency and reliab.lity of the entire procedure. These extracted features are passed to the early fusion and Db.can for feature fusion and optimization. Then deep b.lief, network is employed for recognition. Experimental results demonstrate a separate experiment’s detection average recognition rate of 87% in the HMDb.1 video datab.se and 89% in the YouTub. datab.se, showing a b.tter perspective than the current methods in sports and event identification. © 2024 The Authors. Pub.ished b. Tech Science Press.

关键词： extremal regions joint points Machine learning scalab.e keypoints silhouettes

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