Focusing on the issue that the accuracy of night-time vehicle detection is relatively low, a method of accurately detecting the night-time vehicles by constructing a Gaussian Mixture Model (GMM) for the geometric relationship of the headlights and an AdaBoost (Adaptive Boosting) classifier using inverse projected vehicle samples was proposed. Firstly, the inverse projection plane was set according to the spatial position relation of the headlights in the traffic scene, and the headlights area was roughly positioned by the image preprocessing. Secondly, the geometrical relationship of the headlights was used to construct the GMM with the inverse projected images, and the headlights were initially matched. Finally, the vehicles were detected by using the AdaBoost classifier for inverse projected vehicle samples. In the comparison experiments with the AdaBoost classifier for the original image, the proposed method increased detection rate by 1.93%, decreased omission ratio by 17.83%, decreased false detection rate by 27.61%. Compared with D-S (Dempster-Shafer) evidence theory method, the proposed method increased detection rate by 2.03%, decreased omission ratio by 7.58%, decreased false detection rate by 47.51%. The proposed method can effectively improve the relative detection accuracy, reduces the interference of ground reflection and shadow, and satisfies the requirements of reliability and accuracy of night-time vehicle detection in traffic scene.