Real-time nonparametric background modeling using moving histogram method for visual surveillance

Abstract

The background and foreground modeling is essential in tracking objects from the scenes taken by the stationary camera. We suggest a background model using moving histogram method. A moving histogram, which can be called pixel-wise approach, is time-dependent and can be regarded as a probability density function (pdf) of intensity in image sequence. This moving histogram is updated using image sequence from a stationary camera and is used to calculate the probability of which a pixel in incoming image belongs to background model. Pixels failed in entering into the background model can be candidates for foreground objects. These pixels are classified into foreground ones by comparing with other candidate pixels in different image frames. For pixel classification, our background process consists of queue memory which stores recently acquired images. The background process updates moving histogram for each (x, y) pixel and computes maximum frequency pixel value with low computation. After updating the moving histogram, the background process classifies each pixel as the moving pixel or the background pixel. The classification is difficult because of the slow change in background brightness, slow moving objects, clutters, and the shadow. We solve this problem heuristically. The moving histogram consists of several models (multi-modal, vehicle, background, shadow, clutter). We can compute the distance between the incoming pixel value and each model. And we use threshold with Euler numbers for foreground segmentation. The background and the segmentation process need small computation and can be adapted easily to real-time system.