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Visual surveillance from low-altitude airborne platforms plays a key role in urban traffic surveillance. Moving vehicle detection and motion analysis are very important for such a system. However, illumination variance, scene complexity, and platform motion make the tasks very challenging. In addition, the used algorithms have to be computationally efficient in order to be used on a real-time platform. To deal with these problems, a new framework for vehicle detection and motion analysis from low-altitude airborne videos is proposed. Our paper has two major contributions. First, to speed up feature extraction and to retain additional global features in different scales for higher classification accuracy, a boosting light and pyramid sampling histogram of oriented gradients feature extraction method is proposed. Second, to efficiently correlate vehicles across different frames for vehicle motion trajectories computation, a spatio-temporal appearance-related similarity measure is proposed. Compared to other representative existing methods, our experimental results showed that the proposed method is able to achieve better performance with higher detection rate, lower false positive rate, and faster detection speed.