[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

Vision System for Group of Mobile Robots 141
2 Implementation of Vision Algorithm
The algorithm operates in two modes: Learning Mode and Running Mode. This is
typical approach in the domain of computer vision, however there are some efforts
to get rid of the learning phase and to build an algorithm capable of autonomous
online learning [1].
Before the vision algorithm can actually be used it requires short learning
phase. The learning is supervised, however there are no very complex tasks that
the user have to perform. The algorithm requires user’s attention in order to set
proper thresholds for shadow removal and background subtraction algorithms and
for placing the robots and ball in specific place on the playing field to discover
unique color labels denoting every object of interest. As the algorithm is very
robust, the threshold values have quite big tolerance and usually default values are
sufficient. The block diagram of the steps of learning phase of the algorithm is
presented on Figure 2.
Fig. 2. Schematic diagram of the steps of learning phase of the vision algorithm
After finished learning the algorithm is immediately ready for operation. The
block diagram of the running mode of the algorithm is presented on the Figure 3.
The algorithm starts with obtaining RGB image in resolution 320x240. This
size comes from the limitations imposed by the frame grabber hardware used during
the experiments. Resolution 640x480 is available only in interlaced mode and
640x240 has non-square pixels (twice as tall as wide). The next operation done on
the image is shadow removal according to the current background model and selected
threshold. Details of this algorithm are described below in separate section.
Improved input image without shadows is then used to extract foreground
objects by simple thresholding the difference to the current background model. Obtained
binary image is a subject to mathematical morphology operator called dilation,
which significantly improves the continuity of foreground objects, which usually
become worse when an object is placed over a white line drawn on the black playing
field. The dilation operator uses4-neighbourhood and a mask of size 3x3.