Multi-Agent Framework in Visual Sensor Networks
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Research Article Multi-Agent Framework in Visual Sensor Networks ´ ´ O. Perez, M. A. Patricio, J. Carbo, J. Garc´ıa, and J. M. Molina Grupo de Inteligencia Artificial Aplicada, Departamento de Inform´atica, Universidad Carlos III de Madrid, Avda. Universidad Carlos III 22, Colmenarejo, Madrid 28270, Spain Received 4 January 2006; Revised 13 June 2006; Accepted 13 August 2006 Recommended by Ching-Yung Lin The recent interest in the surveillance of public, military, and commercial scenarios is increasing the need to develop and deploy intelligent and/or automated distributed visual surveillance systems. Many applications based on distributed resources use the socalled software agent technology. In this paper, a multi-agent framework is applied to coordinate videocamera-based surveillance. The ability to coordinate agents improves the global image and task distribution efficiency. In our proposal, a software agent is embedded in each camera and controls the capture parameters. Then coordination is based on the exchange of high-level messages among agents. Agents use an internal symbolic model to interpret the current situation from the messages from all other agents to improve global coordination. Copyright © 2007 M. A. Patricio et al. 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.
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INTRODUCTION
Nowadays, surveillance camera systems are applied in transport applications, such as airports [1, 2], sea environments [3, 4], railways, underground [5–9], and motorways to observe traffic [10–14] in public places, such as banks, supermarkets, homes, department stores [15–19], and parking lots [20–22] and in the remote surveillance of human activities such as football match attendance [23] or other activities [24–26]. The common processing tasks that commercial systems perform are intrusion and motion detection [27–32] and packages detection [28, 31, 32]. Research in university groups tends to improve image processing tasks by generating more accurate and robust algorithms for object detection and recognition [22, 33–37], tracking [22, 26, 33, 38–41], human activity recognition [42–44], database [45–47], and tracking performance evaluation tools [48]. Third-generation surveillance systems [49] is the term sometimes used in the literature to refer to systems conceived to deal with a large number of cameras, a geographical spread of resources, many monitoring points, as well as to mirror the hierarchical and distributed nature of the human process of surveillance. From an image processing point of view, they are based on the distribution of processing capacities over the network and the use of embedded signal-processing devices to get the benefits of scalability and potential robustness provided by distributed systems. The main goals that are
expected of a generic third-generation vision surveillance application, based on end-user requirements, are that it shoul
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