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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. |
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camera parameter dynamic estimation, dense stereo, discriminative classifiers, gradient orientation histograms, intelligent vehicles, intensity-only classification, joint feature space, linear support vector machines, pedestrian classification, pedestrian detection system, regions-of-interest generation, road profile, spatial feature extraction, static scene constraints, feature extraction, image classification, object detection, stereo image processing, support vector machines, traffic engineering computing |
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