Hybrid Deep Learning for Crime Anomaly Detection: Integrating CNN and LSTM for Predictive Analysis of Urban Safety

Abstract

Urban safety has become a growing concern as crime rates rise, necessitating the development of effective systems for crime anomaly detection. Traditional crime monitoring systems often rely on manual observation, static surveillance mechanisms, or rule-based systems, which are limited in scalability, adaptability, and efficiency. Hybrid deep learning approaches, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, offer a transformative solution for predictive analysis of urban safety. CNN excels at extracting spatial features from video footage and images, while LSTM processes temporal sequences, making this integration particularly suited for real-time anomaly detection.