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Human Detection via Classification on Riemannian Manifolds

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3 Author(s)
Tuzel, O. ; Rutgers Univ., Piscataway ; Porikli, F. ; Meer, P.

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.

Published in:

Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on

Date of Conference:

17-22 June 2007

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