Abstract
Abstract. Mapping of parking spaces in cities is a prerequisite for future applications in parking space management like community-based parking. Although terrestrial or vehicle based sensors will be the favorite data source for parking space mapping, airborne monitoring can play a role in building up city wide basis maps which include also parking spaces on ancillary and suburban roads. We present a novel framework for automatic city wide classification of vehicles in moving, stopped and parked using aerial image sequences and information from a road database. The time span of observation of a specific vehicle during an image sequence is usually not long enough to decide unambiguously, whether a vehicle stopped e.g. before a traffic light or is parking along the road. Thus, the workflow includes a vehicle detection and tracking method as well as a rule-based fuzzy-logic workflow for the classification of vehicles. The workflow classifies stopped and parked vehicles by including the neighbourhood of each vehicle via a Delaunay-Graph. The presented method reaches correctness values of around 86.3%, which is demonstrated using three different aerial image sequences. The results depend on several factors like detection quality and road database accuracy.
Highlights
Every drive ends sometime, and mostly in a parking lot
The time span of observation of a specific vehicle during an image sequence, which takes for example up to 20 seconds corresponding to a usual aircraft flight speed and height, is usually not long enough to decide unambiguously, whether a vehicle stopped e.g. before a traffic light or is parking along the road (Knottner et al, 2019)
We propose a new methodology to distinguish between moving, stopping and parking vehicles by exploiting the information contained in aerial image sequences and by using the information of a road database like OpenStreetMap
Summary
Mostly in a parking lot. Map providers and automobile industry take high efforts to solve the problem of time consuming search for parking spaces. The time span of observation of a specific vehicle during an image sequence, which takes for example up to 20 seconds corresponding to a usual aircraft flight speed and height, is usually not long enough to decide unambiguously, whether a vehicle stopped e.g. before a traffic light or is parking along the road (Knottner et al, 2019). In view of this short observation times, we developed a rule-based fuzzy-logic framework to decide about the status of each vehicle. The double-blind peer-review was conducted on the basis of the full paper
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