Background: Maintaining an up-to-date record of the number, type, location, and condition of high-quantity low-cost roadway assets such as traffic signs is critical to transportation inventory management systems. While, databases such as Google Street View contain street-level images of all traffic signs and are updated regularly, their potential for creating an inventory databases has not been fully explored. The key benefit of such databases is that once traffic signs are detected, their geographic coordinates can also be derived and visualized within the same platform. Methods: By leveraging Google Street View images, this paper presents a new system for creating inventories of traffic signs. Using computer vision method, traffic signs are detected and classified into four categories of regulatory, warning, stop, and yield signs by processing images extracted from Google Street View API. Considering the discriminative classification scores from all images that see a sign, the most probable location of each traffic sign is derived and shown on the Google Maps using a dynamic heat map. A data card containing information about location and type of each detected traffic sign is also created. Finally, several data mining interfaces are introduced that allow for better management of the traffic sign inventories.
They also introduced a roadway inventory management system based on the detection and classification of traffic signs from Google Street View images REF .
Detection, classification, and mapping of U.S. traffic signs using google street view images for roadway inventory management
Visualization in Engineering
Engineering