This paper presents a method for detecting moving objects effectively in the weather whose visibility is bad, such as in a snowfall or in a dense fog. In such weather, the visibility changes rapidly in a short time and the intensity of each pixel changes hard every frame. In order to overcome these problems, the proposed method divides an input image into grid regions and in each region, calculates a cross correlation between two histograms whose accumulated number of frames are different. A short accumulated histogram, generated from accumulating a few number of frames, changes quickly whenever moving objects go into the region. On the other hand, a long accumulated histogram, generated from accumulating the more number of frames, changes slowly. Therefore, moving objects are detected by measuring a variation on a cross correlation between a short accumulated histogram and a long accumulated histogram. Experimental results obtained with heavy snow images have shown the effectiveness of the proposed method.