Real-Time Framework for Talent Swimmer Detection

Fakher, Hossam; Badr, Alsayed; Hassanein, Ahmed; Sweidan, Sara

doi:10.21608/ijaici.2025.414064.1015

Real-Time Framework for Talent Swimmer Detection

Document Type : Original Article

Authors

¹ Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

² Department of Information Systems, College of Information Technology, Misr University for Science & Technology, Giza, Egypt. Department of Scientific Computing, Faculty of Computer and Artificial Intelligence, Benha University, Benha, Egypt;

³ Department of Kinesiology, Specifications Biomechanics, Faculty of Physical Education, Dumyat University;

⁴ Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt. Faculty of Computer Science and Engineering, New Mansoura University, New Mansoura, Egypt;

10.21608/ijaici.2025.414064.1015

Abstract

This study presents a real-time framework for swimmer talent identification that integrates state-of-the-art pose estimation and machine learning classification techniques. To address the limitations of traditional pose estimation methods in aquatic environments, RTMPose is employed to extract reliable 2D joint keypoints. Temporal consistency across sequences is achieved using the RIFE interpolation model, selected for its efficiency in standardizing frame counts while avoiding the computational overhead of temporal deep learning models such as LSTMs or 3D CNNs. The dataset, consisting of underwater breaststroke footage, was augmented and balanced using SMOTE, with sensitivity analysis highlighting both its benefits for minority classes and the risk of overfitting. A comprehensive evaluation of twelve classifiers demonstrated that ensemble methods, particularly LightGBM, achieved superior results, yielding a cross-validation F1 score of 93.6% and a test F1 score of 96.8%. While the framework shows strong promise for practical use in sports analytics, its current evaluation is limited to breaststroke and underwater footage. Future work will expand to multiple swimming styles, above-water perspectives, and diverse pool environments to ensure broader generalization.

Keywords