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.

a b s t r a c tComputer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application.

In this paper we present an extension of Cooperative Surveillance Multi-Agent System (CS-MAS) architecture to incorporate dynamic coalition formation. A specific coalition formation using fusion skills is shown so that the fusion process is distributed now in two layers: (i) a global layer in the fusion center, which initialize the coalitions and (ii), local layer within coalitions, with the dynamic instantiation of a local fusion agent. There are several types of autonomous agents: surveillancesensor agents, fusion center agent, local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillance-sensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitation related to specific sensor nature aspects. In this work we add a new autonomous agent called local fusion agent to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent it is dynamically created by the fusion center agent and involves several surveillance-sensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantee that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.

Ambient Intelligence (AmI) aims at the development of computational systems that process data acquired by sensors embedded in the environment to support users in everyday tasks. Visual sensors, however, have been scarcely used in this kind of applications, even though they provide very valuable information about scene objects: position, speed, color, texture, etc. In this paper, we propose a cognitive framework for the implementation of AmI applications based on visual sensor networks. The framework, inspired by the Information Fusion paradigm, combines a priori context knowledge represented with ontologies with real time single camera data to support logic-based high-level local interpretation of the current situation. In addition, the system is able to automatically generate feedback recommendations to adjust data acquisition procedures. Information about recognized situations is eventually collected by a central node to obtain an overall description of the scene and consequently trigger AmI services. We show the extensible and adaptable nature of the approach with a prototype system in a smart home scenario.

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