title: Pedestrian detection via classification on Riemannian manifolds.
abstract: We 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.
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. otuzel@caip.rutgers.edu
authors.forename: Oncel
authors.initials: O
authors.lastname: Tuzel
authors.forename: Fatih
authors.initials: F
authors.lastname: Porikli
authors.forename: Peter
authors.initials: P
authors.lastname: Meer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*; D016138:Walking; D051598:Whole Body Imaging / Q000379:methods*