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.
Highlights
Networks of video cameras are being envisioned for a variety of applications and many such systems are being installed
To understand the activities observed by a multicamera network, the first step is to infer the spatial organization of the environment under surveillance, which can be achieved by camera node localization [1], camera calibration [2, 3], or camera network topology inference [4,5,6,7] for different purposes
The continuous learning mechanism proposed in the paper is necessary for the topology inference to reflect the latent dynamically changing characteristics
Summary
Networks of video cameras are being envisioned for a variety of applications and many such systems are being installed. Most work in computer vision has concentrated on a single or a few cameras While these techniques may be useful in a networked environment, more is needed to analyze the activity patterns that evolve over long periods of time and large swaths of space. Rather than learning the geometrically accurate maps by networked camera localization [1], the objective of topology inference is to determine the topological map of the nodes in the environment. The applications of the inferred camera network topology may include coarse localization of the networked cameras, anomalous activity detection in a multi-camera network, and multiple object tracking in a network of distributed cameras with non-overlapping FOVs. In this paper we develop (i) a multi-layered network architecture that allows analysis of activities at various resolutions, (ii) a method for learning the network topology in an unsupervised manner by integrating visual appearance.
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