title: Characterizing humans on Riemannian manifolds.
abstract: In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the covariance of features. Covariances have been employed for pedestrian detection purposes, actually a binary classification problem on Riemannian manifolds. In this paper, we show how to extend to the multiclassification case, presenting a novel descriptor, named weighted array of covariances, especially suited for dealing with tiny image representations. The extension requires a novel differential geometry approach in which covariances are projected on a unique tangent space where standard machine learning techniques can be applied. In particular, we adopt the Campbell-Baker-Hausdorff expansion as a means to approximate on the tangent space the genuine (geodesic) distances on the manifold in a very efficient way. We test our methodology on multiple benchmark datasets, and also propose new testing sets, getting convincing results in all the cases.
authors.affiliation: Dipartimento di Informatica, University of Verona, Strada le Grazie 15, 37134 Verona, Italy. diego.tosato@univr.it
authors.forename: Diego
authors.initials: D
authors.lastname: Tosato
authors.forename: Mauro
authors.initials: M
authors.lastname: Spera
authors.forename: Marco
authors.initials: M
authors.lastname: Cristani
authors.forename: Vittorio
authors.initials: V
authors.lastname: Murino
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*