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. 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. 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. 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.