HOG纹理因其良好的鲁棒性，在纹理描述中被广泛使用。提出了一种将HOG纹理应用于十字路口全天候车尾检测的算法。即分别采集了白天和夜间该场景下的车尾作为正样本、非车辆和车辆的一部分作为负样本，经预处理后，提取较低维数的HOG纹理送入支持向量机进行训练，得到白天和夜间的识别模型，在检测中根据一定的条件进行切换。对多段视频进行测试证明，该种算法对不同时段的交通场景都具有较高的稳定的车尾识别率，且优于单模型的识别效果。

HOG feature is widely utilized in the description of features for its well robustness. And in this article, an algorithm of applying HOG feature to all-weather vehicle tail detection in crossroads is proposed. Samples in the daytime and in the night-time are collected respectively, while sub-images contain full vehicle tail are extracted from the scenario as positive samples, and non-vehicle sub-images as well as those contain only vehicle parts are taken as negative samples. The low-dimension HOG features are calculated in the positive samples and negative ones, and then are classified by supportive vector machine based on their sample labels after the pre-processing. In consequence, the recognition models are required, for the daytime and for the night-time respectively. The model will be shifted in detection phase in terms of certain conditions. Several video tests prove that this algorithm shows relatively high stability and accuracy in vehicle tail detection in different time interval, in comparison with the one using single recognition model.