  Since their appearance, Smart Cities have aimed at improving the daily life
of people, helping to make public services smarter and more efficient. Several
of these services are often intended to provide better security conditions for
citizens and drivers. In this vein, we present Heimdall, an AI-based video
surveillance system for traffic monitoring and anomalies detection. The
proposed system features three main tiers: a ground level, consisting of a set
of smart lampposts equipped with cameras and sensors, and an advanced AI unit
for detecting accidents and traffic anomalies in real time; a territorial
level, which integrates and combines the information collected from the
different lampposts, and cross-correlates it with external data sources, in
order to coordinate and handle warnings and alerts; a training level, in charge
of continuously improving the accuracy of the modules that have to sense the
environment. Finally, we propose and discuss an early experimental approach for
the detection of anomalies, based on a Faster R-CNN, and adopted in the
proposed infrastructure.
