jise Journal of Innovative Science and Engineering 2602-4217 Bursa Technical University 10.38088/jise.1703936 Image Processing Görüntü İşleme A Hybrid 3D CNNs Transformer Architecture for Video-Based Human Action Recognition with Improved Accuracy on UCF101 and HMDB51 https://orcid.org/0000-0002-7994-2679 Seven Engin İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA https://orcid.org/0000-0003-1979-8860 Yücel Demirel Eylem ISTANBUL UNIVERSITY-CERRAHPASA, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, COMPUTER ENGINEERING PR. 12 15 2025 9 2 327 342 05 21 2025 09 29 2025 Copyright © 2017, Journal of Innovative Science and Engineering 2017 Journal of Innovative Science and Engineering

Video-Based Human Action Recognition (HAR) remains challenging due to inter-class similarity, background noise, and the need to capture long-term temporal dependencies. This study proposes a hybrid deep learning model that integrates 3D Convolutional Neural Networks (3D CNNs) with Transformer-based attention mechanisms to jointly capture spatio-temporal features and long-range motion context. The architecture was optimized for parameter efficiency and trained on the UCF101 and HMDB51 benchmark datasets using standardized preprocessing and training strategies. Experimental results indicate that the proposed model reaches 97% accuracy and 96.8% mean F1-score on UCF101, and 85% accuracy, and 83.8% F1-score on HMDB51, showing consistent improvements compared to the standalone 3D CNNs and Transformer variants under identical settings. Ablation studies confirm that the combination of convolutional and attention layers significantly improves recognition performance while maintaining competitive computational cost (3.78M parameters, 17.75 GFLOPs/video, ~7 ms GPU latency). These findings highlight the effectiveness of the hybrid design for accurate and efficient HAR. Future work will address class imbalance using focal loss or weighted training, explore multimodal data integration, and develop more lightweight Transformer modules for real-time deployment on resource-constrained devices.

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