Abstract: A depth estimation algorithm from monocular vehicle infrared image based on depth estimation model by supervised learning was proposed. Firstly, kernel-based principle component analysis(KPCA) was used to select infrared image features. Original features extracted from infrared image were project nonlinearly to a high dimensional and linear separable feature space using kernel function. Principle component analysis(PCA) was performed to get dimension reduction infrared image features. Then the infrared image features and depth values were trained using BP neural network. A depth estimation model was obtained which can estimate the depth distribution of monocular vehicle infrared image. The experimental results show that most of the depth estimated by the model is consistent with the original depth information of infrared image.