INTEGRATING LOCAL AND GLOBAL FEATURES FOR VEHICLE DETECTION IN HIGH RESOLUTION AERIAL IMAGERY
Stefan Hinz · 2003
This paper introduces a new approach to automatic vehicle detection in monocular high resolution aerial images. The extraction relies upon both local and global features of vehicles and vehicle queues, respectively. To model a vehicle on local level, a 3D-wireframe representation is used that describes the prominent geometric and radiometric features of cars including their shadow region. The model is adaptive because, during extraction, the expected saliencies of various edge features are automatically adjusted depending on viewing angle, vehicle color measured from the image, and current illumination direction. The extraction is carried out by matching this model ”top-down ” to the image and evaluating the support found in the image. On global level, the detailed local description is extended by more generic knowledge about vehicles as they are often part of vehicle queues. Such groupings of vehicles are modeled by ribbons that exhibit the typical symmetries and spacings of vehicles over a larger distance. Queue extraction includes the computation of directional edge symmetry measures resulting in a symmetry map, in which dominant, smooth curvilinear structures are searched for. By fusing vehicles found using the local and the global model, the overall extraction gets more complete and more correct. In contrast to most of the related work, our approach neither relies on external information like digital maps or site models, nor it is limited to very constrained environments as, e.g., highway scenes. Various examples of complex urban traffic scenes illustrate the applicability of this approach. However, they also show the deficiencies which clearly define the next steps of our future work. 1