Citilog is here to highlight how its Automatic Incident Detection (AID) leverages deep learning to address this issue by training AI models on vast datasets of real-world traffic incidents. By analysing thousands of incident examples, the system can filter out environmental factors such as shadows, rain, and other weather conditions, reducing false positives by a factor of 10. As a result, Citilog’s AID can more accurately identify valid road hazards, enhancing the reliability of the system. For traffic operators, this means valuable time is saved by focusing on incidents that require attention instead of sifting through numerous camera feeds or false alarms.
The technology goes further by identifying types of incidents, including wrong-way driving, stopped vehicles, pedestrians and cyclists, debris, slow vehicles, and congestion. It can even detect smoke in tunnels before a fire starts, saving precious minutes over infrared-only systems. This differentiation ability enables the quick deployment of emergency responders, potentially saving lives.
Citilog’s AID software solution is compatible with many existing camera models being used to monitor critical roadways and is ideal for highways, bridges, and tunnels.
Stand H6-A4
