Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors

  • Liliana Lo Presti
  • Stan Sclaroff
  • Marco La Cascia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Establishing correspondences among object instances is still challenging in multi-camera surveillance systems, especially when the cameras’ fields of view are non-overlapping. Spatiotemporal constraints can help in solving the correspondence problem but still leave a wide margin of uncertainty. One way to reduce this uncertainty is to use appearance information about the moving objects in the site. In this paper we present the preliminary results of a new method that can capture salient appearance characteristics at each camera node in the network. A Latent Dirichlet Allocation (LDA) model is created and maintained at each node in the camera network. Each object is encoded in terms of the LDA bag-of-words model for appearance. The encoded appearance is then used to establish probable matching across cameras. Preliminary experiments are conducted on a dataset of 20 individuals and comparison against Madden’s I-MCHR is reported.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Liliana Lo Presti
    • 1
  • Stan Sclaroff
    • 2
  • Marco La Cascia
    • 1
  1. 1.Dipartimento di Ingegneria InformaticaUniversity of PalermoItaly
  2. 2.Computer Science DepartmentBoston UniversityUSA

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