Flir Intelligent Transportation Systems will be equipping both the Prado Carénage Tunnel and the Prado Sud Tunnel in Marseille with Automatic Incident Detection (AID) technology.
57 cameras in the Prado Sud tunnel and 95 cameras in the Prado Carénage tunnel are monitoring traffic for all types of incidents, including stopped vehicles, wrong way drivers, pedestrians and smoke. All of the information, provided by a combination of cameras and encoders, is collected and managed by the Flux video detection management system.
Flux collects traffic data, events, alarms and video images generated by the video detectors. The main goal of Flux is to manage and control all traffic information generated by these various detectors and to make it useful, meaningful and relevant to the user. Flux provides a user-friendly interface composed of a monitoring and a reporting application, and enables real-time monitoring of events and alarms.
The 2.5km long Prado Carénage Tunnel allows drivers to cross the city of Marseille without leaving the motorway; connecting the Southern districts of the city and the Eastern motorway to the city centre, and to the North via the Vieux Port tunnel and the coastal motorway. The 1.5km double-deck Prado Sud tunnel, each with two lanes, will extend the existing Prado Carénage tunnel towards the southern part of the city. The tunnel is scheduled to open in 2014.
‘For this project, Flir Systems is offering a complete detection and monitoring solution, including automatic incident detection, surveillance cameras, mobile cameras, recording capabilities and visualisation,’ commented Mario Pinto, area manager at Flir Intelligent Transportation Systems. ‘With the addition of this contract, our technology is now monitoring all current tunnels in the city of Marseille.’
For these tunnels, Flir Systems offers high-quality video servers with integrated video analytics, as well as an integrated automatic incident detection system. For the Prado Sud tunnel, the choice of the technology fits into the growing trend of edge-based analytics, where the video analytics functionality is positioned closer to the traffic camera and to the edge of the network.
