Embedded Parking Slot Detection and Classification for Smart City Scenarios

As cities are becoming more crowded due to the population moving to urban areas, smart parking and management are essential elements of the transportation system. There are efforts in developing vision-based automated parking systems by detecting the ground markings accurately in real-time from multiple surround-view cameras. We are not interested in automated parking, but in collecting relevant parking occupancy statistics from the city streets. In this paper, we propose a novel method based on a single front-view camera and a convolutional neural network (CNN) for detecting the parking slots and classifying them based on occupancy. This information collected by the camera is sent to a central processing cloud and can serve as a basis for many Smart City parking management applications, by providing useful dynamic occupancy statistics.