Urban areas are expected to grow explosively in the coming years, which requires intelligent decentralized monitoring and control to improve traffic flow and resource allocation. Current cameras and computer-vision algorithms for traffic monitoring exist and perform well, but they require expensive and power-hungry GPU hardware. On a large scale, the power efficiency of neuromorphic systems (brain inspired sensing and computation) can save Gigawatts in energy and millions of EUR.
Here, we combine research on object tracking in event cameras with neuromorphic processors to track urban traffic in real-time with unprecedented power efficiency and accuracy. This is a valuable commodity for the optimization of urban environments, and directly improves the citizens’ quality of life.
Our project explores the useability and the market for urban traffic analysis, demonstrates merits by comparing computation and power requirements, and implements a proof-of-principle demonstrator system on neuromorphic hardware.