Roadwatch: An Integrated Architecture for AI-Powered Surveillance and Anomaly Detection in Traffic Areas

Saia R.;Podda A. S.;Pompianu L.;Marras M.;Floris N.;Carta S.

2026-01-01

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Abstract

The increasing complexity of urban traffic management necessitates advanced surveillance and anomaly detection solutions. The proposed Roadwatch architecture is a comprehensive system integrating Artificial Intelligence (AI) and Computer Vision to monitor vehicular and pedestrian traffic effectively. By leveraging interconnected software and hardware components, Roadwatch identifies objects captured by cameras and provides essential meta-information for detecting specific anomalies. This assists in crucial tasks such as traffic management, pedestrian safety, incident detection, and optimal resource allocation for urban infrastructure. AI plays a pivotal role in processing camera feeds, enabling object detection, identification, and tracking. With configurable rule-based detection, Roadwatch offers an innovative approach to intelligent surveillance, enhancing safety and security in diverse real-world traffic environments while utilizing existing camera infrastructure.