In this paper we propose a vehicle detection framework on low resolution aerial range data. Our system consists of three steps: data mapping, 2D vehicle detection and postprocessing. First, we map the range data into 2D grayscale images by using the depth information only. For this purpose we propose a novel local ground plane estimation method, and the estimated ground plane is further refined by a global refinement process. Then we compute the depth value of missing points (points for which no depth information is available) by an effective interpolation method. In the second step, to train a classifier for the vehicles, we describe a method to generate more training examples from very few training annotations and adopt the fast cascade Adaboost approach for detecting vehicles in 2D grayscale images. Finally, in post-processing step we design a novel method to detect some vehicles which are comprised of clusters of missing points. We evaluate our method on real aerial data and the experiments demonstrate the effectiveness of our approach.
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