为了构建鲁棒的背景模型和提高前景目标检测的准确性，综合考虑视频图像在同一位置上像素点的时间相关性和邻域像素的空间相关性，本文提出一种基于多特征融合的背景建模方法，用单帧图像中像素的邻域相关性快速建立初始背景模型，利用视频图像序列像素值、频数、更新时间和自适应敏感度更新背景模型，有效改善了ghost 现象，减少运动目标的孔洞和假前景。通过多组数据测试，表明本算法提高了对动态背景、复杂背景的适应性和鲁棒性。

In order to build a robust background model and improve the accuracy of detection of foreground objects, the temporal correlation of pixels at the same position of the video image and the spatial correlation of neighboring pixels are considered comprehensively. This paper proposed a background modeling method based on multi-feature fusion. By using the domain correlation of pixels in a single frame image to quickly establish an initial background model whichis updated using pixel values, frequency, update time and sensitivity of the video image sequence, the ghost phenomenon is effectively improved and the holes and false prospects for moving targets are reduced. Through multiple sets of data tests, it shows that the algorithm improves the adaptability and robustness of dynamic background and complex background.