Human Behaviour Recognition in Video Surveillance Systems Based on Neural Networks

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Abstract: "Human behaviour recognition plays a critical role in intelligent video surveillance systems for security, crowd management, and abnormal activity detection. This paper explores the application of neural networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), for recognizing and classifying human behaviours in video surveillance footage. We propose a novel deep learning-based approach to extract features from video frames and temporal data, achieving high accuracy in behaviour recognition tasks. Experimental results on benchmark datasets demonstrate the effectiveness of our model in detecting various behaviours, including walking, running, and suspicious activities, even in complex environments."