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In this paper we propose a novel part-based framework for pedestrian detection. We model a human as a hierarchy of fixed overlapped parts, each of which described by covariances of features. Each part is modeled by a boosted classifier, learnt using Logitboost on Riemannian manifolds. All the classifiers are then linked to form a high-level classifier, through weighted summation, whose weights are...
Several pixel-based people counting methods have been developed over the years. Among these the product of scale-weighted pixel sums and a linear correlation coefficient is a popular people counting approach. However most approaches have paid little attention to resolving the true background and instead take all foreground pixels into account. With large crowds moving at varying speeds and with the...
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