Integrated Pedestrian Classification and Orientation Estimation

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Integrated Pedestrian Classification and Orientation Estimation
This paper presents a novel approach to single-frame pedestrian classification and orientation estimation. Unlike previous work which addressed classification and orientation separately with different models, our method involves a probabilistic framework to approach both in a unified fashion. We address both problems in terms of a set of view-related models which couple discriminative expert classifiers with sample-dependent priors, facilitating easy integration of other cues (e.g. motion, shape) in a Bayesian fashion. This mixture-of-experts formulation approximates the probability density of pedestrian orientation and scalesup to the use of multiple cameras. Experiments on large real-world data show a significant performance improvement in both pedestrian classification and orientation estimation of up to 50%, compared to stateof-the-art, using identical data and evaluation techniques.
Markus Enzweiler, Dariu Gavrila
Added 30 Aug 2010
Updated 30 Aug 2010
Type Conference
Year 2010
Where CVPR
Authors Markus Enzweiler, Dariu Gavrila
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