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SS05 - Multi-Level Fusion: bridging the gap between high and low level fusion

The exploitation of all relevant information originating from a growing mass of heterogeneous sources, both device-based (sensors, video, etc.) and human-generated (text, voice, etc.),  is a key factor for the production of timely, comprehensive and most accurate description of a situation or phenomenon. There is a growing need to effectively identify relevant information from the mass available, and exploit it through automatic fusion for timely, comprehensive and accurate situation awareness. Even if exploiting multiple sources, most fusion systems are developed for combing just one type of data (e.g. positional data) in order to achieve a certain goal (e.g. accurate target tracking) without considering other relevant information that could be of different origin, type, and with possibly very different representation (e.g. a priori knowledge, contextual knowledge, mission orders, risk maps, availability and coverage of sensing resources, etc.) but still very significant to augment the knowledge about observed entities. Very likely this latter type of information could be considered of different fusion levels that rarely end up being systematically exploited automatically. The result is often stove-piped systems dedicated to a single fusion task with limited robustness.  This is caused by the lack of an integrative approach for processing sensor data (low-level fusion) and semantically rich information (high-level fusion) in a holistic manner thus effectively implementing a multi-level processing architecture and fusion process. The proposed special session will bring together researchers working on fusion techniques and algorithms often considered to be at different and disjunct, fostering thus the discussion on the commonalities and differences in their research methodologies, and proposing viable multi-level fusion solutions to address challenging problems or relevant applications.

Topics of interest

  • Theory and Representation: Probability theory, Dempster-Shafer theory, logic based fusion, classifiers, statistical relational learning, data mining, ontologies;  
  • Algorithms: Target tracking and localization; situation assessment; pattern analysis; behavior modeling; predictive and impact  assessment; process management; sensor/resource management; hard and soft data fusion.
  • Architectures: Frameworks to interconnect multiple fusion processes, uncertainty representation and interchange, multi-level reasoning.
  • Applications: Defence and intelligence, security, medicine, emergency response, safety monitoring, economy, robotics.

Special Session Organizers

  • Lauro Snidaro, University of Udine (Italy)
  • Jesus Garcia, University Carlos III of Madrid (Spain)
  • Wolfgang Koch, Fraunhofer FKIE (Germany)

Special Session Contact