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.

IEEE Trans. Pattern Anal. Machine Intell, 30, 1713-1727, 2008.
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