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).
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Borca-Mureşan, R., Nedevschi, S., Măguran, F. (2009). Mixtures of Classifiers for Recognizing Standing and Running Pedestrians. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2008. Lecture Notes in Computer Science, vol 5337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02345-3_34
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DOI: https://doi.org/10.1007/978-3-642-02345-3_34
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