Empirical Evaluation of Video Surveillance based Crime and Anomaly Detection System using Hybrid Deep Learning Strategy

In the realm of urban safety and security, the development of efficient and accurate crime detection systems is of paramount importance. This study presents an empirical evaluation of a cutting-edge Video Surveillance-based Crime and Anomaly Detection System (CADS) that harnesses the power of Hybrid Deep Learning, integrating Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN). CADS has been meticulously designed to detect and analyze criminal activities and abnormal behaviors in real-time video surveillance data. It employs a hybrid approach that combines the capabilities of ANN for feature extraction and pattern recognition with the temporal analysis prowess of RNN for identifying sequential anomalies. Through rigorous empirical evaluation, CADS demonstrates remarkable performance in urban crime detection scenarios. The system excels in detecting both overt and subtle criminal activities, providing valuable insights for enhanced safety measures. Notable achievements include the recognition of suspicious behaviors such as crowd contraflow, prolonged parking lot scouting, and even direct assaults. This research delves into the intricate workings of CADS, elucidating its design and implementation in Python on a Windows platform. The proposed model represents a significant advancement in the field of crime and anomaly detection when compared to existing models. In rigorous comparative evaluations, the proposed model has demonstrated its superiority by consistently outclassing all existing models, achieving an impressive accuracy rate of 97.77%. By leveraging CADS, law enforcement agencies and urban security stakeholders can proactively respond to potential threats and anomalies, ultimately contributing to safer and more secure urban environments.