UNIVERSITÀ DEGLI STUDI DI PARMA Studio e applicazione di ...

A.7. Validation and results 99
it is a false positive, is low; at the same time, when a bounding box is discarded,
the risk it contains a pedestrian is not negligible. Therefore, a single vote for each
validator is not sufficient to evaluate the presence of a pedestrian.
Figura A.33: Results of the probabilistic model validators: detected pedestrians are
shown using a superimposed red box when the confidence on the classification is
high, or blue when the confidence is low.
Figure A.33 shows a few results of the validator based on the probabilistic model.
If the confidence on the detection is above a threshold, the corresponding box is
drawn in red, meaning that the region has a high probability of containing a human
shape, or in blue when the confidence is low.
Figure A.34 shows the results of the symmetry computation step. This allows
both to refine bounding boxes width, to split bounding boxes that contain more than
one object and to validate them, filtering out edgeless or asymmetric bounding boxes.
Some symmetrical objects, like cars are trees, are validated as well. Moreover, some
validation problems are encountered when the far infrared images are not optimal,
like those acquired in summer under heavy direct sunlight; in these conditions, many
object in the background become warm, and the assumption that a pedestrian features
a higher temperature than the background is not satisfied. This causes some problems