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High-Level Information Fusion in Visual Sensor Networks
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Author(s): Juan Gómez-Romero (University Carlos III of Madrid, Spain), Jesús García (University Carlos III of Madrid, Spain), Miguel A. Patricio (University Carlos III of Madrid, Spain), José M. Molina (University Carlos III of Madrid, Spain)and James Llinas (University at Buffalo, USA)
Copyright: 2012
Pages: 27
Source title:
Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications
Source Author(s)/Editor(s): Li-Minn Ang (University of Nottingham Malaysia Campus, Malaysia)and Kah Phooi Seng (University of Nottingham Malaysia Campus, Malaysia)
DOI: 10.4018/978-1-61350-153-5.ch010
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Abstract
Information fusion techniques combine data from multiple sensors, along with additional information and knowledge, to obtain better estimates of the observed scenario than could be achieved by the use of single sensors or information sources alone. According to the JDL fusion process model, high-level information fusion is concerned with the computation of a scene representation in terms of abstract entities such as activities and threats, as well as estimating the relationships among these entities. Recent experiences confirm that context knowledge plays a key role in the new-generation high-level fusion systems, especially in those involving complex scenarios that cause the failure of classical statistical techniques –as it happens in visual sensor networks. In this chapter, we study the architectural and functional issues of applying context information to improve high-level fusion procedures, with a particular focus on visual data applications. The use of formal knowledge representations (e.g. ontologies) is a promising advance in this direction, but there are still some unresolved questions that must be more extensively researched.
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