当前景目标与背景在颜色上接近时，仅采用高斯混合模型进行目标检测容易导致误判。为了提高模型分割算法的鲁棒性，提出一种融合高斯混合模型和小波变换的运动目标检测算法。通过小波变换提取图像的纹理特征信息，利用高斯混合模型拟合背景信息。将两者融合起来，把纹理信息作为颜色信息的补偿，保证了模型在线更新背景信息时模型的稳定性和收敛性，同时弥补了目标分割中前景与背景颜色信息接近时容易导致误判的不足。实验结果表明，本文方法比经典高斯混合模型方法具有较高的分割精度。

Commission errors often arise when Gaussian mixture models (GMMs) are applied to detect moving objects in situations where foreground and background have similar colors.To improve the robustness of the segmentation method，a moving object detection method is proposed by combining a Gaussian mixture model with the wavelet transform.The wavelet transform is employed to extract texture information and a GMM is employed to update the background.Color information and texture information are integrated for segmentation.The method improves the abilities of convergence and stability，and also decreases commission errors occuring in methods which only use color information.Experimental results indicate that the proposed method is superior to the traditional GMM.