A Comprehensive Systematic Review of Deep Learning Techniques for Anomaly Detection in Urban Video Surveillance

The rapid evolution of urban surveillance systems has created an urgent need for advanced anomaly detection methods capable of interpreting complex public environments. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to evaluate deep learning's (DL) role in video-based anomaly detection. It contrasts conventional approaches with cutting-edge architectures like spatiotemporal convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models. Our analysis demonstrates DL's superior performance over traditional methods across multiple benchmarks while revealing significant implementation challenges in real-world deployment, including computational complexity, cross-domain generalization, and ethical constraints. The study provides a comprehensive taxonomy of anomaly types, examines key evaluation metrics for operational systems, and identifies emerging solutions like edge-compatible lightweight models and privacy-preserving federated learning. By synthesizing a decade of research progress and practical limitations, this work offers actionable insights for developing robust, efficient, and socially responsible surveillance systems. The study proposes future directions focused on self-supervised learning, multimodal sensor fusion, and explainable AI frameworks to address critical gaps in urban security applications.