An Object- and Task-Oriented Architecture for Automated Video Surveillance in Distributed Sensor Networks

In this paper, an agent-based software architecture for automated wide area video surveillance systems is presented. The proposed concept is designed for detection and tracking of moving objects across multiple camera views. The surveillance system consists of a decentralized collaborative sensor network with object- and task-oriented architecture. At sensor node level, image processing algorithms are applied for event and object detection. In case of detection (e. g. motion) an agent-based multi-sensor processing cluster is created. Each instantiated cluster is responsible for observation of one object in the scene. Object handover is managed autonomously by the dynamic sensor clusters. The dynamic sensor clustering approach allows adding new sensors without resetting the system parameters, which is a big advantage in large sensor networks. Furthermore, by using the agent-based architecture it is possible to create a framework with an adaptive data and processing load. Additionally, upgrade of system capabilities can be done easily updating or adding new processing agents. The proposed concept has been proved on an experimental video surveillance system at the Fraunhofer IITB.