|Supervisor:||Dr.-Ing. Gilbert Yammine|
|Faculty:||Prof. Dr.-Ing. André Kaup|
|Info:||Quality monitoring and error detection in systems is a major step in the|
testing phase of a system before making it available to the consumer.
In new infotainment systems present in luxurious cars, system errors
are not accepted and should be avoided. Consequently, a monitoring
system should be built to record the data shown on the infotainment
screen and an analysis of the data should be done to detect errors and
find their sources.
One frequent system error is image freezing, i.e. motion stop of the navigation
map due to high CPU loads or other error sources. In order to
detect such an artifact, the difference image between frames could be
used to signal any motion in the frames. When no motion exists, this
can mean that either the car has stopped or a freezing has occurred. In
order to differentiate between both cases, the map position should be
calculated before and after the motion stop, and map jumps could then
be detected. For that, invariant points on the map that exist before and
after the freeze should be found and map rotations and translations
could then be calculated by solving the affine transformation equation.
The task of Mr. Khairat is to test different invariant feature detectors
and descriptors such as Harris, SIFT, SURF, and FAST, and compare
their performance when used for tracking of translating and rotating navigation
maps considering different map display styles. Two performance
criteria have to be tested: the robustness of the features after rotation
and translation of the map, and the computation time for each detector/
descriptor. Additionally, Mr. Khairat has to implement a method
to select the best feature points and track the motion of the map. He
will have then to test his algorithm on different navigation sequences
(with and without freezing) and report its performance for various map
The algorithms have to be implemented in MATLAB and a clean documentation
of the work and of the source code will be highly assessed.