Vision-based pedestrian detection and estimation with a blind corner camera

Research Paper (undergraduate) from the year 2006 in the subject Electrotechnology, grade: 1,0, University Karlsruhe (TH), language: English, abstract: Avoiding collision accidents is becoming more and more an important topic in the research
field of driver assistant systems. Especially for vision-based detection systems there are
various approaches, which are built upon many different methods.
This thesis deals with the avoidance of pedestrian accidents, caused by Blind Corner
view problems. The presented approach comprises a pedestrian detection subsystem, which
is part of a large camera system framework covering observation of the car environment.
Based on a Blind Corner Camera and a neural network classification method, research in
this thesis is focused on two aspects: detection improvement and danger level estimation.
Since vision-based classification methods usually are still not able to yield perfect results,
because of the complexity of this task, the detection result has to be improved by
preprocessing and post processing. In this work, first, effects of image enhancement
methods on detection are tested as preprocessing methods and, secondly, a new approach
for a simple tracking and estimation strategy is presented, which improves detection in the
way of a post processing method. Finally, information from tracking and prediction is used
to estimate a danger level for pedestrians, which provides information about how collisionprone
the current situations is.