Objective: Today's computers can do many amazing things but there are still many "trivial" but important tasks they cannot do well. In particular, current information extraction techniques perform well when event types are well represented in the training data but often fail when encountering information-rich unexpected rare events. DIRAC project addresses this crucial machine weakness and aims at designing and developing an environment-adaptive autonomous artificial cognitive system that will detect, identify and classify possibly threatening rare events from the information derived by multiple active information-seeking audio-visual sensors. Biological organisms rely for their survival on detecting and identifying new events. DIRAC therefore strives to combine its expertise in physiology of mammalian auditory and visual cortex and in audio/visual recognition engineering with the aim to move the art of audiovisual machine recognition from the classical signal processing/pattern classification paradigm to human-like information extraction. This means, among other things, to move from interpretation of all incoming data to reliable rejection of non-informative inputs, from passive acquisition of a single incoming stream to active search for the most relevant information in multiple streams, and from a system optimized for one static environment to autonomous adaptation to new changing environments, thus forming foundation for a new generation of efficient cognitive information processing technologies. DIRAC is an EU IP IST project of the 6th Framework Program. Its duration is 5 years, from January 2006 until December 2010.
Partners:IDIAP Research Institute (CH) The Hebrew University of Jerusalem (IL) Czech Technical University (CS) Carl von Ossietzky Universitaet Oldenburg (DE) Leibniz Institute for Neurobiology (DE) Katholieke Universiteit Leuven, Laboratorium voor Neuro- en Psychofysiologie and ESAT/PSI VISICS (B) Oregon Health and Science University OGI School of Science and Engineering (USA).
Finished in: 2010