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Institute
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.
In diesem Beitrag wird eine Methode entwickelt, die auf Basis der automatischen Verkehrserfassung des Deutschen Zentrums für Luft- und Raumfahrt e.V. (DLR), stehende Fahrzeuge in „parkend“ und „am Verkehr teilnehmend“ unterteilt. Bei der Erfassung, die Teilbereich des Projektes VABENE++ ist, werden während eines Helikopterfluges Fahrzeuge im Bild detektiert und über eine Bildsequenz getrackt, sodass anschließend für jedes wiedergefundene Fahrzeug mehrere Positionen vorliegen. Im Zuge dessen werden zahlreiche Verkehrsparameter gewonnen. Anhand dieser Daten wird die finale Unterscheidung mithilfe von Fuzzy-Logik realisiert. Neben den gegenwärtigen Umgebungseinflüssen werden dabei auch die benachbarten Fahrzeuge mit einbezogen.