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

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Author(s): Zou Xiaotao | Bhanu Bir | Roy-Chowdhury Amit

Journal: EURASIP Journal on Image and Video Processing
ISSN 1687-5176

Volume: 2009;
Issue: 1;
Start page: 460689;
Date: 2009;
Original page

ABSTRACT
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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