1. A method for long-term understanding of background modeling, the method comprising: determining a first dimension gradient model derivative of image brightness data of an image pixel along a first dimension of two-dimensional image data, and a second dimension gradient model derivative of the image brightness data of the image pixel along the second dimension of the two-dimensional image data, wherein the image brightness data is a single channel of color information extracted from video image data of a static image scene; averaging via a processing unit the determined gradients with any previous determined gradients of the image pixel, and with gradients of each of a plurality of neighboring pixels as a function of the respective distances of the neighboring pixels to the image pixel, the averaging generating averaged pixel gradient models for each of a plurality of pixels of the video image data of the static image scene that each have mean values and weight values; and constructing via the processing unit background models for the static image scene as a function of the averaged pixel gradient model mean values and weight values, wherein each pixel in the background model is represented by a set of the averaged pixel gradient models that each have similar orientation and magnitude and have a weight meeting a background threshold weight requirement.