Mixtures of Classifiers for Recognizing Standing and Running Pedestrians

Authors:
Raluca Borca-Mureşan;Sergiu Nedevschi;Florin Măguran
Affiliations:
Technical University of Cluj-Napoca, Cluj-Napoca, Romania;Technical University of Cluj-Napoca, Cluj-Napoca, Romania;Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Venue:
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Year:
2008

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Abstract

Recognizing pedestrians in traffic scenarios is an important task for any smart vehicle application. Within the context of a real-time stereo based driving assistance system, this paper presents a novel method for recognizing pedestrians. We have designed a meta- classification scheme composed of a mixture of Bayesian and boosted classifiers that learn the discriminant features of a pedestrian space partitioned into attitudes like pedestrian standing and pedestrian running. Our experiments show that the mixture of classifiers proposed outperforms a single classifier trained on the whole un-partitioned object space. For classification we have used a probabilistic approach based on Bayesian Networks and Adaptive Boosting. Two types of features were extracted from the image: anisotropic gaussians and histograms of gradient orientations (HOG).