摘要: |
An exciting new machine vision technology has emerged to complement the current vehicle detection technology in Intelligent Transportation Systems applications. Machine vision sensor technology offers several advantages over conventional in-pavement sensors in traffic management. However, current systems do not meet certain needs of advanced traffic management. Whereas virtually all the deployed and operational machine vision sensors are multi-camera units, there are potential applications where multiple cameras are not needed. Examples in which multi-camera units may not be appropriate are downtown intersections of one-way streets, work zone monitoring, data collection, and arterial status monitoring. A new class of machine vision sensor is emerging to fill this need. This new sensor integrates the camera optics with an image processor to offer the traffic engineer choices such as incident detection, queue size measurement, turning movement extraction, vehicle tracking, and traditional loop emulation in a compact single-camera package. Such an integrated unit does not require a relatively expensive multi-camera processor box or chassis. It also reduces infrastructure installation cost by eliminating the need for the transmission of high bandwidth video from the camera to the processor. It establishes a new standard and opens up exciting possibilities of a whole new breed of systems that could lead to much wider scale accelerated deployment of non- intrusive, wide area sensors. This new technology is currently being deployed in downtown Minneapolis in partnership with the Minnesota Department of Transportation. The deployment is a part of the Adaptive Urban Signal Control and Integration operational test of the Split Cycle Offset Optimization Technique adaptive control sponsored by the Federal Highway Administration. |