Human Detection via Classification on Riemannian Manifolds
Oncel Tuzel (1,3) Fatih Porikli (3) and Peter Meer (1,2)
(1)Department of Computer Science
(2)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(3) Mitsubishi Electric Research Laboratories
Cambridge, MA 02139
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.
2007 Computer Vision and Pattern Recognition Conference, Minneapolis,
Minnesota, June 2007.
