Pedestrian Detection via Classification

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 learn-ing 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 contri-bution 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 the INRIA and Daim-lerChrysler pedestrian data sets where superior detection rates are observed over the previous approaches