Improving NIC algorithm using different binary structure elements for multi-modal foreground detection

This paper improves a remarkable background estimation algorithm, namely Neighbor-based Intensity Correction (NIC) which is used in the background subtraction technique for foreground detection. The algorithm has an efficient intensity correction scheme to update the current background based on calculating the standard deviation of two windows captured from the background and the input frame in which the windows are constructed by a squared-structure binary mask. Although the NIC algorithm achieved the comparative results with existing approaches on the foreground detection accuracy and processing speed, its performance in the multi-modal background including high-speed motion and camera jitter should be improved. In the original algorithm, we recognize that the shape of a binary mask further affects the updating performance besides the window size which was already analyzed. Various shapes are therefore recommended for the multi-modal background adaptation. Moreover, an adaptive threshold identified by referring several previous Otsu thresholds to cope with the high-speed motion challenge is proposed. Experimental results on some standard datasets such CAVIAR 2004, AVSS2007, PETS 2009, and CDNET 2014, demonstrate that the foreground detection accuracy is significantly boosted with 2.6--6.7% of the F-measure metric.