Abstract
This paper introduces a new approach on automatic vehicle detection in monocular large scale aerial images. The extraction is based on a hierarchical model that describes the prominent vehicle features on different levels of detail. Besides the object properties, the model comprises also contextual knowledge, i.e., relations between a vehicle and other objects as, e.g., the pavement beside a vehicle and the sun causing a vehicle’s shadow projection. 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 specific vehicle models. Various examples illustrate the applicability and flexibility of this approach. However, they also show the deficiencies which clearly define the next steps of our future work.
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Hinz, S., Baumgartner, A. (2001). Vehicle Detection in Aerial Images Using Generic Features, Grouping, and Context. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_7
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DOI: https://doi.org/10.1007/3-540-45404-7_7
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