Image Processing Reference
Fig. 8.5 ( a ) A static background image. The two arrows indicate the position of two pixels.
( b ) Histograms of the pixel values at the two positions. The data come from a sequence of im-
Background subtraction is a simple and yet efficient method of extracting an object
in a scene. This is especially true if the background can be designed to be uniform.
In indoor and controlled setups this is indeed realistic, but for more complicated
scenarios, other methods might be necessary. Even in the case of a controlled setup
two issues must be considered:
1. Is the background really constant?
2. How to define the threshold value, which is used to binarize the difference image?
When you point a video camera at a static scene, for example a wall, the images
seem the same. Very often, however, they are not. The primary reason being that
artificial lighting seldom produces a constant illumination. Furthermore, if sunlight
enters the scene, then this will also contribute to the non-constant illumination due
to the randomness associated with the incoming light rays. The effect of this is
illustrated in Fig. 8.5 . To the left an image from a static scene is shown. To the
right two histograms are shown. The first histogram is based on the pixel values at
position #1 for a few seconds and similar for the second histogram. If the images are
actually the same, the histograms would contain only one non-zero bin. As can be
seen this is not the case and in general no such thing as a static background exists.
Say that the pixel at position #2 in the first image of the video sequence has a
value of 80 (not very likely according to the histogram, but nevertheless possible).
If the first image is used as the reference image, then typical background images