Motion-inclusive skip connection gated recurrent unit: An end-to-end deep neural network for action recognition

Understanding human actions from video streams remains a technically demanding endeavor, yet it is foundational to domains such as automated monitoring, behavioral analytics, and interactive system design. Consequently, research is steadily shifting toward automated frameworks capable of reliably interpreting video-based information. Prior studies have focused on separating spatial cues and temporal dependencies through diverse algorithmic strategies, often treating motion feature extraction as a standalone process outside the end-to-end learning pipeline. To resolve this constraint, a novel neural framework based on Gated Recurrent Units ( GRUs ) is developed to effectively integrate motion dynamics, spatial representations, and temporal evolution within a unified model. Skip connections are incorporated to counteract vanishing gradients, thereby ensuring smoother gradient flow and enhanced training stability. The proposed architecture is thoroughly evaluated on five established benchmark datasets, namely UCF101, HMDB51, Hollywood2, UCF50, and SS1, where it delivers state-of-the-art classification accuracies of 99.57%, 91.36%, 94.68%, 99.92%, and 80.37%, respectively. The experimental outcomes demonstrate the high precision, robustness, and versatility of the model across a variety of realistic application settings. Furthermore, we performed rigorous evaluations under various noisy conditions to assess the resilience of the proposed GRU architecture. The outcomes consistently demonstrated its strong resistance to noise and reinforced its reliability in challenging environments.