Machine Learning Based Mobile Capacity Estimation for Roadside Parking

Abstract. The growing number of cars and limited street space present significant challenges for cities, applying not only to moving but extending to stationary traffic. The quest for parking spaces exacerbates traffic congestion, noise, and air pollution, particularly in residential areas. To develop effective parking solutions for these challenges, a trustful data foundation on available parking space capacities, its usage and parking type is crucial. Gathering this data is currently time-consuming, requiring manual labeling and street inspections. Moreover, it must be repeated to keep the data current. Research on parking space management has heavily focused on monitoring designated parking lots with fixed cameras to identify free or occupied parking spaces. However, due to privacy concerns fixed cameras are not applicable for the larger part of the street space in European cities. This paper introduces a novel computer vision-based method for automatically collecting parking space capacities and parking type information. Our approach combines both street view and aerial imagery, which are recorded by a moving camera source. We tackle challenges in geo-referencing images, identifying parking types, classifying moving and stationary cars and dealing with partial occlusions in images. By not permanently recording the same environment, our approach lowers the surveillance risk, making parking capacity estimation scalable. We conduct a thorough evaluation of our methods and release a novel validation data set to allow for further research. In the future, more moving camera sources will be available when attached to city cleaning vehicles or to delivery drones.