目的 背景建模在计算机视觉领域中是检测、跟踪、行为学习和识别的基础，被广泛地应用于视频监控的运动目标检测。混合高斯（MOG）和Codebook是其中具有代表性的方法，但它们假设像素点间信息是独立的，只保留了时域信息而忽略了空域信息，使得模型对背景的描述局限于时间上的连续性。针对上述问题，提出了一种自适应邻域相关性的背景建模方法（ANC）。方法 ANC在保留原始方法时域信息建模特性的同时，增加对邻域模型的复用，同时利用计算结果反馈自适应调整邻域区域，提高对前景值判断的准确性。首先利用原始基于像素点的背景建模方法进行候选前景检测，然后将候选前景检测结果为前景点的像素与邻域像素点模型进行对比，若邻域范围存在匹配则为背景点，若不存在则为前景点；最后引入像素置信度概念，自适应调整邻域范围的大小。结果 与MOG和Codebook相比，在changedetection标准数据库上，ANC在ROC（受试者工作特征曲线）和度量值等方面的平均精度和F-measure都提高了7%以上。结论 自适应邻域相关性的背景建模方法适用于复杂多模态背景，克服了基于像素点背景建模方法假设的局限性。与普通基于像素点的背景建模方法相比，具有更好的鲁棒性和抗噪性，对复杂背景具有更强的适应性。

Objective Background modeling is widely used to detect moving objects and is the basis for object tracking, behavior learning, and recognition in the field of computer vision. Mixture of Gaussian (MOG) and Codebook are current popular methods based on pixel value. However, these methods usually assume that pixels are independent and retain only time domain information while ignoring spatial information, limiting the model to the continuity of time. This paper proposes an adaptive neighborhood correlation (ANC) background modeling approach. Method The ANC approach increases the neighborhood model while retaining the domain information, and considers results to adjust neighborhood area. ANC begins by using the original pixel-based background modeling method to detect the candidate foreground; it then further compares the foreground results of candidate foreground detection with models of neighborhood pixels, with matched pixels considered as background pixels, while others foreground pixels. Finally, pixel confidence is introduced to adjust the neighborhood size adaptively. Result ANC outperforms MOG and Codebook by more than 7% in average accuracy and F-measure with the ROC curve and other aspects of the measures on change detection standard database. Conclusion ANC overcomes the limitations of pixel-based background modeling methods and is suitable for a complex multimodal background. It not only describes the change in pixels accurately, but is also robust and adaptive to the complex background.