That is where Citilog’s Applied Deep Learning (ADL) comes in which will be demonstrated in Grapevine. Here’s how it works: video-based automated incident detection from Citilog is fed thousands of actual examples of traffic incidents - wrong way driving, stopped vehicles, smoke in tunnels, pedestrians and cyclists, debris, slow vehicles, and congestion.
This database of incidents trains the AI to be more effective and efficient at recognizing authentic events. Citilog says it is the equivalent of sending your AI off to university for a degree in literal street smarts. The result is a dramatic reduction in false positive alerts caused by environmental factors such as shadows, snow, rain, and other weather conditions, improving accuracy by a factor of ten.
The Maryland Transportation Authority in the US is using ADL today to proactively respond to incidents in mere seconds. Meanwhile, the New York State Bridge Authority is in the process of upgrading its incident detection system with Citilog ADL to improve operational efficiency and deploy emergency responders even faster.
Armed with this automated accuracy, agencies employing Citilog’s ADL have the potential to revolutionise traffic safety and traffic management for their citizens.