Although many different types of technologies for information systems have evolved over the last decades (such as databases, video systems, the Internet and mobile telecommunication), the integration of these technologies is just in its infancy and has the potential to introduce "intelligent" systems. This paper describes the novelties of a video content analysis in a surveillance system, demonstrating the benefits for fast retrieval in huge video databases.
High-level Video content analysis such as video-surveillance is often limited by computational aspects of automatic image understanding, i.e. it requires huge computing resources for reasoning processes like categorization and huge amount of data to represent knowledge of objects, scenarios and other models.This article explains how to design and develop a "near real-time adaptive image datamart", used, as a decisional support system for vision algorithms, and then as a mass storage system. Using RDF specification as storing format of vision algorithms meta-data, we can optimise the data warehouse concepts for video analysis, add some processes able to adapt the current model and pre-process data to speed-up queries. In this way, when new data is sent from a sensor to the data warehouse for long term storage, using remote procedure call embedded in object-oriented interfaces to simplified queries, they are processed and in memory data-model is updated. After some processing, possible interpretations of this data can be returned back to the sensor.To demonstrate this new approach, we will present typical scenarios applied to this architecture such as people tracking and events detection in a multi-camera network. Finally we will show how this system becomes a high-semantic data container for external data-mining.
Today' s technologies in video analysis use state of the art systems and formalisms like onthologies and datawarehousing to handle huge amount of data generated from low-level descriptors to high-level descriptors. In the IST CARETAKER project we develop a multi-dimensional database with distributed features to add a centric data view of the scene shared between all the sensors of a network.We propose to enhance possibilities of this kind of system by delegating the intelligence to a lot of other entities, also known as "Agents" which are specialized little applications, able to walk across the network and work on dedicated sets of data related to their core domain. In other words, we can reduce, or enhance, the complexity of the analysis by adding or not feature specific agents, and processing is limited to the data concerned by the processing.This article explains how to design and develop an agent oriented systems which can be used by a video analysis datawarehousing. We also describe how this methodology can distribute the intelligence over the system, and how the system can be extended to obtain a self reasoning architecture using cooperative agents. We will demonstrate this approach.
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