Accurate recognition of human abnormal behaviours using adaptive 3D residual attention network with gated recurrent units (GRU) in the video sequences

Abnormal or violent behaviour by individuals with mental disorders presents significant risks to public safety, necessitating advanced systems capable of detecting such behaviours in real time. Traditional single-sensing methods for human activity recognition often struggle with issues like signal noise, dropped data, and limited scalability, which hinder their ability to accurately detect abnormal behaviours in dynamic and complex environments. This paper introduces a novel solution that addresses these challenges by proposing an adaptive 3D residual attention network (A3D-RAN) combined with Gated Recurrent Units (GRUs). The A3D-RAN utilises an adaptive attention mechanism to focus on the most relevant regions in video sequences, while residual connections improve feature reuse and maintain gradient flow, enabling fine-grained detail capture. GRUs are integrated to efficiently model long-term temporal dependencies, ensuring a more comprehensive understanding of human behaviour across time. Through extensive experimentation on real-world datasets, our model achieved a remarkable accuracy of 97%, significantly surpassing the 78% accuracy of standalone A3D-RAN implementations. Moreover, the robustness of the model under challenging conditions - such as occlusions and lighting variations - demonstrates its potential for real-world surveillance applications. By employing the Improved War Strategy Optimization (IWSO) Algorithm for parameter tuning, we further enhanced performance, reaching an unprecedented accuracy of 99%. This breakthrough underscores the practical value of our approach in improving public safety and security through accurate and timely detection of abnormal behaviours in surveillance systems.