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Schlagwörter:
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Zusammenfassung:
This paper presents a novel pedestrian detection system for intelligent
vehicles. We propose the use of dense stereo for both the generation of regions
of interest and pedestrian classification. Dense stereo allows the dynamic
estimation of camera parameters and the road profile, which, in turn, provides
strong scene constraints on possible pedestrian locations. For classification,
we extract spatial features (gradient orientation histograms) directly from
dense depth and intensity images. Both modalities are represented in terms of
individual feature spaces, in which discriminative classifiers (linear support
vector machines) are learned. We refrain from the construction of a joint
feature space but instead employ a fusion of depth and intensity on the
classifier level. Our experiments involve challenging image data captured in
complex urban environments (i.e., undulating roads and speed bumps). Our
results show a performance improvement by up to a factor of 7.5 at the
classification level and up to a factor of 5 at the tracking level (reduction
in false alarms at constant detection rates) over a system with static scene
constraints and intensity-only classification.
