Master Thesis - Hochschule Bonn-Rhein-Sieg

5. Algorithms Master Thesis Björn Ostermann page 74 of 126
The filter’s algorithm searches for pixels that are considered free. It then looks at the neighbouring
pixels, to find intrusions. If an intrusion is found, it is compared to the minimum value in that pixel
that was measured during the background acquisition (see chapter 5.2.1). If the value is greater than
that minimum value, the pixel is considered as fluctuation noise and set to free space, since that value
is likely to be caused by the background.
The distance around free pixels, in which the algorithm searches for fluctuation noise can be
configured.
As Figure 51 shows, the search-area around free pixels, in which occupied pixels can be found and
eliminated, can be larger than the one of the spike filter, which only searches in neighbouring pixels.
The larger the search-area, the more pixels are checked that belong to intruding objects. This effect is
compensated by the fact that this filter keeps values that are definite positives and eliminates only
those of which are behind the minimal background distance. Although this filter may cause reduction
in the size of objects, this only occurs with objects that are close to the background. The reduction
must still be considered in the safety approval of the system (see chapter 6.3). This leads to far better
results than the spike filter.
a) b)
free pixel found
intrusion above minimal distance
intrusion below minimal distance
Figure 51: a) probability filter size 1, b) probability filter size 2
Intrusion map, red = no intrusion, dark green = intrusion, light green = noise
In this work only the smallest size of this filter, the search in neighbouring pixels, needed to be used,
to attain sufficient results. The intrusion map, after application of this filter, is shown in Figure 52.