With the development of low cost aerial optical sensors having a spatial resolution in the range of few centimetres, the traffic monitoring by plane receives a new boost. The gained traffic data are very useful in various fields. Near real-time applications in the case of traffic management of mass events or catastrophes and non time critical applications in the wide field of general transport planning are considerable. A major processing step for automatically provided traffic data is the automatic vehicle detection. In this paper we present a new processing chain to improve this task. First achievement is limiting the search space for the detector by applying a fast and simple pre-processing algorithm. Second achievement is generating a reliable detector. This is done by the use of HoG features (Histogram of Oriented Gradients) and their appliance on two consecutive images. A smart selection of this features and their combination is done by the Real AdaBoost (Adaptive Boosting) algorithm. Our dataset consists of images from the 3K camera system acquired over the city of Munich, Germany. First results show a high detection rate and good reliability
