Multi-class classification on Riemannian manifolds for video surveillance

Authors:
Diego Tosato;Michela Farenzena;Marco Cristani;Mauro Spera;Vittorio Murino
Affiliations:
Dipartimento di Informatica, University of Verona, Italy;Dipartimento di Informatica, University of Verona, Italy;Dipartimento di Informatica, University of Verona, Italy and Istituto Italiano di Tecnologia, Genova, Italy;Dipartimento di Informatica, University of Verona, Italy;Dipartimento di Informatica, University of Verona, Italy and Istituto Italiano di Tecnologia, Genova, Italy
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Year:
2010

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Abstract

In video surveillance, classification of visual data can be very hard, due to the scarce resolution and the noise characterizing the sensors' data. In this paper, we propose a novel feature, the ARray of COvariances (ARCO), and a multi-class classification framework operating on Riemannian manifolds. ARCO is composed by a structure of covariance matrices of image features, able to extract information from data at prohibitive low resolutions. The proposed classification framework consists in instantiating a new multi-class boosting method, working on the manifold Symd+ of symmetric positive definite d × d (covariance) matrices. As practical applications, we consider different surveillance tasks, such as head pose classification and pedestrian detection, providing novel state-of-the-art performances on standard datasets.