KITCHENER, Ontario--(BUSINESS WIRE)--Miovision, a global leader in smart city technology, today launched the next generation of traffic technology. Using a type of artificial intelligence (AI) called deep learning, Miovision SmartSense brings AI to the roadside to help cities sense and understand what's happening at the intersection in real time. SmartSense can detect the presence and movement of vehicles, pedestrians and cyclists and use this data to improve congestion and safety. The new Miovision SmartSense technology completes the company's TrafficLink solution, which also includes a 360-degree fisheye camera and an IoT connected hub that allows traffic professionals to remotely access the intersection. Together, these components make up a powerful AI toolkit that uncovers insights about the intersection.

Nobody likes it, but we all have to deal with it. As the world's cities grow more densely populated, scientists and entrepreneurs are looking for solutions to gridlock, pollution and the other byproducts of a world filled with cars. Two sessions at the GPU Technology Conference earlier this month spoke to the role that data, deep learning and intelligent video analytics can play in easing traffic and improving quality of life for city dwellers the world over. Kurtis McBride, CEO of Miovision Technologies, an IVA startup based in Ontario, Canada, spoke to a room full of developers about his company's efforts -- and their 40 percent year-over-year growth -- to make traffic flow a little easier. Miovision's Open City platform gets data from existing city infrastructure and the company's own video cameras, and applies AI to create insights from it.

Back in college at the University of Waterloo, Kurtis McBride used to spend his days sitting at the side of the road, counting cars. It was one of his many tasks as an intern at a traffic engineering company--tallying the cars that drove through the intersection and reporting the data back to his bosses, who would use the intel to shorten a red light here, extend a red light there. Three years later, he says, they would do it all over again. For a young engineer eager to exercise his technical chops, this approach seemed archaic. "We're taking a combination of data that's collected once every three years for a day and is accurate if you're lucky, and we're deploying it on 30-year-old infrastructure," he says. "We're not taking advantage of the progress we've seen in other industries."