Community based repository for georeferenced traffic signs

Abstract

Traffic sign maintenance requires periodic on-site inspection to determine if signs are in good conditions and visible, both day and night. However, periodic inspections are time and cost consuming. Another issue is related to the drivers awareness to the traffic signs on the road. Many factors may potentially contribute to a driver missing a sign, such as the sign being damaged or occluded, or distraction caused by the many gadgets inside the vehicle. We propose a dual purpose community based approach. On the one hand, each driver can use his mobile device to detect, recognize and geolocate traffic signs, contributing to the traffic sign central repository. Detection is performed using cascade classifiers, while a convolutional neural network support the recognition phase. The repository, based on the information received from the clients, can be used to provide reports about sign status, preventing the need for global inspections and providing the information required for more direct and timely inspections. On the other hand, the drivers would have access to the database of traffic signs therefore being able to receive real-time notifications regarding traffic signs such as speed limit signs, school proximity, or road construction signs.