Urban Context Aware Neural Surveillance: A Case Study in Dense Metropolitan Environments

Neural network performance in surveillance tasks is highly dependent on contextual factors such as urban density, lighting conditions, and behavioral patterns. This study presents an end-to-end monitoring solution optimized for deployment in high-density metropolitan areas. Drawing on real-world testing in an Asian megacity, the system was evaluated under peak pedestrian and vehicular load, achieving 91.2% recall and 95.6% precision in anomaly classification. The framework leverages spatiotemporal feature extraction and adaptive thresholding to maintain consistent performance across varied city zones. Our findings highlight the value of urban-context-aware calibration in deploying neural-based monitoring at scale.