Pedestrian 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 pedestrians 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 geometry of the space. The algorithm is tested on
INRIA and DaimlerChrysler pedestrian datasets where superior
detection rates are observed over the previous approaches.
Accepted for publication in IEEE Trans. Pattern Anal. Machine Intell.
