City-scale threat trajectory aggregation across heterogeneous camera networks: a conceptual deep learning framework

This article presents a conceptual deep learning framework for city-scale threat trajectory aggregation across heterogeneous surveillance networks. The proposed architecture addresses the challenges of detecting and tracking potential threats across thousands of uncalibrated cameras and urban sensor systems, including CCTV cameras, drones, license plate readers, acoustic sensors, and access-control infrastructure. The framework combines federated edge inference, dynamic topology graphs, cross-modal data fusion, and cross-jurisdiction information propagation to enable large-scale threat analysis while minimizing the exchange of raw video data. Additionally, the paper introduces a network-level anomaly detection component designed to identify coordinated multi-actor threats that cannot be detected through individual trajectory analysis alone. The work is conceptual and focuses on architectural design, scalability considerations, privacy-preserving mechanisms, and future directions for experimental validation.