In general, cars are rectangular shape in the aerial images, so the histograms of orient gradient over the whole sliding window were computed to find the primary gradient direction and to estimate the orientation of the car in the window, and the detection window was rotated according to the car’s orientation to perform classification. A cascaded boosting classifier and the HOG (histograms of orient gradient) features in the proposed car detection method were employed. To efficiently compute the HOG features in the rotated window, a fast HOG features extraction method based on CFHOG (circle filter based histograms of orient gradient), which was more efficient than the classical HOG extraction method based on integral histograms. In addition, lookup tables are used to speed up the calculation of the orientation partition and magnitude. A set of experiments on real images prove the applicability and high efficiency of the proposed car detection method.