Reinhard Klette, Norbert Krüger, Tobi Vaudrey, Karl Pauwels, Marc van Hulle, Sandino Morales, Farid I. Kandil, Ralf Haeusler, Nicolas Pugeault, Clemens Rabe, Markus LappeAbstractThis paper discusses options for
testing correspondence algorithms in stereo or motion analysis that are
designed or considered for vision-based driver assistance. It introduces
a globally available database, with a main focus on testing on video
sequences of real-world data. We suggest the classification of recorded
video data into situations defined by a cooccurrence of some events in
recorded traffic scenes. About 100-400 stereo frames (or 4-16 s of
recording) are considered a basic sequence, which will be identified
with one particular situation. Future testing is expected to be on data
that report on hours of driving, and multiple hours of long video data
may be segmented into basic sequences and classified into situations.
This paper prepares for this expected development. This paper uses three
different evaluation approaches (prediction error, synthesized
sequences, and labeled sequences) for demonstrating ideas, difficulties,
and possible ways in this future field of extensive performance tests
in vision-based driver assistance, particularly for cases where the
ground truth is not available. This paper shows that the complexity of
real-world data does not support the identification of general rankings
of correspondence techniques on sets of basic sequences that show
different situations. It is suggested that correspondence techniques
should adaptively be chosen in real time using some type of statistical
situation classifiers. [Download] [BibTeX] |
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