Smart Surveillance System for Monitoring and Detecting Abnormal Human Behaviour

The increasing demand for real-time video surveillance systems has underscored the need for automated methods to detect abnormal human behaviors, particularly to enhance the safety of individuals, including those living alone. This study presents a deep learning-based framework for identifying anomalous human activities in video data, enabling timely interventions. By leveraging advanced deep CNNs, the proposed framework extracts discriminative features directly from raw video frames, eliminating dependence on manual design features. To mitigate challenges associated with limited labeled training data, transfer learning from large-scale action recognition datasets is employed. The model is trained and rigorously evaluated on a dataset encompassing four critical categories of abnormal behavior: fighting, explosions, robbery, and assault. The project results to show the framework's effectiveness in accurately detecting these activities, contributing to enhanced surveillance and public safety.