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Pedestrian Detection via Classification on Riemannian Manifolds

Tuzel, Oncel; Porikli, Fatih; Meer, Peter

Description

We present a new algorithm to detect pedestrian in still images utilizing covariance matrices as object descriptors. Since the descriptors do not form a vector space, well known machine learning techniques are not well suited to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. The main contribution of the paper is a novel approach for classifying points lying on a connected Riemannian manifold using the...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
URI: http://hdl.handle.net/1885/33451
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI: 10.1109/TPAMI.2008.75

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