Spatio-temporal background models for object detection

One of the fundamental problems in computer vision is detecting regions or objects of interest from an image sequence. Background subtraction, which removes a background image from the input image, is widely used for detecting foreground objects in practical applications, since it enables us to detect foreground objects without any previous knowledge of them. However, simple background subtraction often detects not only foreground objects but also a lot of noise regions, because it is quite sensitive to background changes. In general, background changes which occur in outdoor scenes can be mainly classified into two types: • Illumination changes – changes caused by lighting conditions such as the sun rising, setting, or being blocked by clouds, • Dynamic changes – changes caused by the swaying motion of tree branches, leaves and grass, fleeting cloud, waves on water and so on.