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CVPR
2007
IEEE

Human Detection via Classification on Riemannian Manifolds

9 years 8 months ago
Human Detection via Classification on Riemannian Manifolds
We present a new algorithm to detect humans in still images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, well known machine learning techniques are not adequate to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. We present a novel approach for classifying points lying on a Riemannian manifold by incorporating the a priori information about the geometry of the space. The algorithm is tested on INRIA human database where superior detection rates are observed over the previous approaches.
Oncel Tuzel, Fatih Porikli, Peter Meer
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Oncel Tuzel, Fatih Porikli, Peter Meer
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