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

Abstract

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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Correspondence to Xiaotao Zou.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zou, X., Bhanu, B. & Roy-Chowdhury, A. Continuous Learning of a Multilayered Network Topology in a Video Camera Network. J Image Video Proc 2009, 460689 (2009). https://doi.org/10.1155/2009/460689

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Keywords

  • Network Topology
  • Face Recognition
  • Directed Graph
  • Discrete Event
  • Traffic Pattern