This article is part of the series Video-Based Modeling, Analysis, and Recognition of Human Motion.

Open Access Research Article

Continuous Learning of a Multilayered Network Topology in a Video Camera Network

Xiaotao Zou*, Bir Bhanu and Amit Roy-Chowdhury

Author Affiliations

Center for Research in Intelligent Systems, University of California, Riverside, CA 92521, USA

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EURASIP Journal on Image and Video Processing 2009, 2009:460689  doi:10.1155/2009/460689

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Received:20 February 2009
Revisions received:18 June 2009
Accepted:23 September 2009
Published:16 November 2009

© 2009 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level) is analyzed both in simulation and in real-life experiments and compared with previous approaches.

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