Anomaly Detection Using Convolutional Neural Networks for Crime Prevention: A Deep Learning Approach

Anomaly detection using Convolutional Neural Networks (CNNs) has emerged as a powerful tool for identifying criminal activities, including robberies, assaults, and homicides, within surveillance environments. This research presents a deep learning-based framework that leverages CNNs for spatial feature extraction and combines them with temporal modelling to recognize irregular behaviour in public safety contexts. By analysing surveillance footage and sensor-based data, the system detects anomalies in movement patterns, crowd density, and object interactions, thereby aiding in real-time threat assessment and crime prevention. The proposed method utilizes pre-trained CNN models for high-level visual representation and integrates hybrid approaches, such as CNN-LSTM and 3D CNNs, to capture spatiotemporal dynamics of suspicious activities. Our framework is tested on benchmark datasets like UCF-Crime and UAV surveillance feeds, achieving high accuracy in detecting abnormal behaviour. Furthermore, anomaly detection is enhanced using advanced feature extraction techniques and real-time classification mechanisms tailored for smart city surveillance systems.