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Leveraging an annotated dataset of 872 high-resolution videos covering 15 state combinations, the model integrates optimized 3D convolutions and residual connections, achieving significant improvements in classification accuracy and computational efficiency. With a test accuracy of 92.78%, the model significantly outperforms 2D CNNs and C3D models. Additionally, its lightweight architecture and reduced computational complexity equip it with the potential for real-time deployment in controlled environments. While challenges such as data imbalance and limited generalization remain, this research provides a robust technical framework for speed climbing video analysis and lays the groundwork for broader applications in spatiotemporal modeling and intelligent sports analytics.<\/jats:p>","DOI":"10.2478\/ijcss-2025-0002","type":"journal-article","created":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T14:05:32Z","timestamp":1740319532000},"page":"17-34","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning with 3D ResNets for Comprehensive Dual-Lane Speed Climbing Video Analysis"],"prefix":"10.2478","volume":"24","author":[{"given":"Y.","family":"Xie","sequence":"first","affiliation":[{"name":"College Of Computing and Information Technologies , National University , Manila , Philippines"},{"name":"School of Social Management , Jiangxi College of Applied Technology , Ganzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Mariano","sequence":"additional","affiliation":[{"name":"College Of Computing and Information Technologies , National University , Manila , Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"2026062802573681566_j_ijcss-2025-0002_ref_001","doi-asserted-by":"crossref","unstructured":"Ahmed, A. 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