Tuzel, OncelPorikli, FatihMeer, Peter2015-12-080162-8828http://hdl.handle.net/1885/33451We present a new algorithm to detect pedestrian 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.Keywords: Artificial intelligence; Covariance matrix; Learning algorithms; Learning systems; Matrix algebra; Boosting; Classification; Daimler-Chrysler; Data-sets; Descriptors; Detection rates; Machine-learning techniques; New algorithm; Object descriptors; Pedestr Boosting; Classification; Object descriptors; Pedestrian detection; Riemannian manifolds; Symmetric positive definite matrices200810.1109/TPAMI.2008.752015-12-08