ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 95–101, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-95-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 95–101, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-95-2019

  16 Sep 2019

16 Sep 2019

RULE-BASED MAPPING OF PARKED VEHICLES USING AERIAL IMAGE SEQUENCES

J. Knöttner2, D. Rosenbaum1, F. Kurz1, P. Reinartz1, and A. Brunn2 J. Knöttner et al.
  • 1German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling, Germany
  • 2University of Applied Sciences Würzburg-Schweinfurt (FHWS), Würzburg, Germany

Keywords: Aerial Image Sequences, Vehicle Detection, Vehicle Tracking, Parking Space Mapping, Fuzzy Logic

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.