Multi-class Classification on Riemannian Manifolds for Video Surveillance

  • Diego Tosato
  • Michela Farenzena
  • Mauro Spera
  • Vittorio Murino
  • Marco Cristani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)


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 \(Sym^{+}_d\) 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.


Riemannian Manifold Tangent Space Sectional Curvature Covariance Matrice Video Surveillance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Diego Tosato
    • 1
  • Michela Farenzena
    • 1
  • Mauro Spera
    • 1
    • 2
  • Vittorio Murino
    • 1
  • Marco Cristani
    • 1
    • 2
  1. 1.Dipartimento di InformaticaUniversity of VeronaItaly
  2. 2.Istituto Italiano di Tecnologia (IIT)GenovaItaly

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