[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

146 A. Babiarz, R. Bieda, and K. Jaskot
2.3 Input Image Format
The vision algorithm operates in RGB color space. It has the advantage of being
directly available from the frame-grabber hardware, so there is no need for color
space conversion. Additionally, working in the RGB color space isintuitive and
the visualization and analysis of RGB images and histograms is very easy for
human eye. The RGB color space however is not ideal and suffers from nonlinear
shift of colors happening after change to the intensity of light. Fortunately, effects
of this undesired shift can be easily removed by the adaptation of the algorithm
which is described below.
In theory, there are other color spaces like HSV or HSL, in which color information
is H and S channels should not be vulnerable to changes in the intensity of
light. However, experiments proved that although they really are better than RGB
color space in preserving the color information unchanged despite of the change of
lighting conditions, HSV or HSL histograms also require adaptation, like for
RGB. So there is really no significant advantage of using these color spaces.
2.4 Adaptation of Background Model
Although the background subtraction algorithm isn’t very sensitive to moderate
changes of ambient lighting, it is desirable to give it the ability to update the background
model in order to ensure its proper operation during long periods of time.
The problem here is not in choosing how to update the background (the moving
average algorithm with learning factor about 0.1 is good enough) but rather in
deciding which pixels should be updated. In theory all pixels classified as belonging
to the background should be the subject to update, practice however shows
that this method leads to pollution of background model with pixels coming from
fuzzy edges and from shadow of objects. The solution is to update only pixels
lying outside of object’s bounding boxes, enlarged twice in order to ensure that no
pixels from object’s shadow or fuzzy edge will contribute to the update of background
model. So the whole procedure of the adaptation of background model can
be expressed in the formula:
, , , 1 , , ,
(8)
, ,
where is a set of pairs , being coordinates of pixels in the image, is an
image being actual background model, is input image, is set of pairs ,
being coordinates of all pixels inside two times enlarged bounding boxes, found
by flood fill algorithm. Enlargement is done in a way that center of bounding box
remains in the same position. Indexes and 1 denote previous and current
frame, respectively.