Volume XL-7/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 139-144, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W2-139-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 139-144, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W2-139-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Oct 2013

29 Oct 2013

Validation of Vehicle Candidate Areas in Aerial Images Using Color Co-Occurrence Histograms

W. Leister1,2, S. Tuermer1,3, P. Reinartz1, K. H. Hoffmann2, and U. Stilla3 W. Leister et al.
  • 1Remote Sensing Technology Institute, German Aerospace Center (DLR) Oberpfaffenhofen, Germany
  • 2Institut fuer Physik, Technische Universitaet Chemnitz 09107 Chemnitz, Germany
  • 3Photogrammetry and Remote Sensing, Technische Universitaet Muenchen (TUM) 80290 Munich, Germany

Keywords: Vehicles, aerial imagery, traffic monitoring, color co-occurrence histograms, 3K+ camera system

Abstract. Traffic monitoring plays an important role in transportation management. In addition, airborne acquisition enables a flexible and realtime mapping for special traffic situations e.g. mass events and disasters. Also the automatic extraction of vehicles from aerial imagery is a common application. However, many approaches focus on the target object only. As an extension to previously developed car detection techniques, a validation scheme is presented. The focus is on exploiting the background of the vehicle candidates as well as their color properties in the HSV color space. Therefore, texture of the vehicle background is described by color co-occurrence histograms. From all resulting histograms a likelihood function is calculated giving a quantity value to indicate whether the vehicle candidate is correctly classified. Only a few robust parameters have to be determined. Finally, the strategy is tested with a dataset of dense urban areas from the inner city of Munich, Germany. First results show that certain regions which are often responsible for false positive detections, such as vegetation or road markings, can be excluded successfully.