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. Historically, crime detection systems relied heavily on human intervention and statistical methods, which were time-intensive and prone to errors. Before the advent of AI, systems such as closed-circuit television (CCTV) surveillance and static alarms were used, but they lacked the intelligence to adapt and predict complex scenarios, leading to inefficiencies and delayed responses. The motivation to develop this paper stems from the urgent need to address these limitations, inspired by advancements in deep learning that enable automated, accurate, and timely crime anomaly detection. Existing systems often fail to process large-scale data effectively, detect subtle anomalies, or adapt to evolving crime patterns, making them insufficient in ensuring urban safety. The proposed hybrid system leverages the strengths of CNN and LSTM to analyse spatial and temporal data from urban environments, enabling accurate crime detection and proactive response. By training the model on real-world datasets, the system can identify anomalies in real time, significantly enhancing the capability of urban safety mechanisms. This paper aims to revolutionize crime detection by addressing the shortcomings of traditional systems, improving urban safety, and setting a benchmark for intelligent anomaly detection in dynamic urban settings.