A Standard Deep Learning-Based Model That Integrates R3D-18 With RNN for Clip Classification and Transition Point Detection in Basketball Video Footages - Aliga Paul; Joshua Nehinbe; Kingsley Eghonghon Ukhurebor (Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE), 2026)
Research on basketball video clip analysis is rapidly emerging as a vital tool for coaching, player development, scouting, recruitment of coaches and players. Such analysis enhances post-game review, tactical planning, practice design, in-game adjustments, visual feedback and decision-making. However, many existing models lack robust architectures for transition detection, limiting their effectiveness. Additionally, high costs, limited customization, internet dependency and complex interfaces restrict widespread adoption of most models across professional and grassroots levels. To address these challenges, this paper implements a deep learning model with Python language. The model integrates Residual 3D Network with 18 layers (R3D-18) to capture spatial-temporal features, Convolutional neural networks (CNNs) to perform feature extraction and Recurrent Neural Network (RNN) to accomplish temporal modeling of offline basketball videos. Using entropy thresholding and top-k (i.e., top-10) accuracy metrics on four basketball datasets, the model performs clip-wise and video-level action classification with exceptional confidence, achieving 99.92% and 99.52% certainty that all the evaluative footages depict basketball videos. The model further detected no transitions in basketball_video1 and basketball_video3 while notable transitions appeared at clips 8 and 7 in basketball_video2 and basketball_video4, respectively corresponding to spikes in uncertainty likely caused by activity changes like switching from basketball to running. Average entropy scores of 4.787, 3.5341, 3.7912 and 3.1976 across datasets were reported, reflecting potentially elusive variations and uncertainties within clip-level predictions due to nuanced activity shifts despite strong overall classification confidence.