Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection

TL;DR

Heimdall is an AI-driven traffic monitoring system for smart cities that detects accidents and anomalies in real time using a multi-tier infrastructure with smart lampposts, data integration, and continuous learning.

Contribution

The paper introduces Heimdall, a novel multi-tier AI infrastructure for real-time traffic anomaly detection and monitoring in smart city environments.

Findings

Early experimental results with Faster R-CNN show promising anomaly detection capabilities.

The system effectively integrates multi-source data for improved traffic monitoring.

Heimdall enhances urban safety through real-time alerts and continuous model improvement.

Abstract

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.