Background subtraction is one of the most commonly used methods for moving target detection in video sequences. A background modeling method integrating image color and texture features is proposed to accurately and quickly complete the background modeling of a video sequence and accurately detect the moving foreground. First, the kernel and mode kernel density estimation methods are used to model the RGB color space and the Haar local binary pattern (HLBP) texture of a video image, and the color and texture models are obtained. Then, the color and texture models are fused by normalization and twice threshold judgment. The color and texture models complement each other to form a background model by setting an appropriate threshold. Finally, the background model is used to detect the moving foreground of the video sequence, and the background model is updated. The experimental results show that the proposed method works well with dynamic backgrounds and shadowed scenes. The proposed method's average F1-score on the test set is 0.8471, which is higher than the common algorithms. The average frame rate is 25.57 frame·s^-1, which meets the real-time requirement.