IS = { zkontrolovano 18 Jan 2009 },
  UPDATE  = { 2009-01-06 },
  author =      {Anem{\" u}ller, J{\" o}rn and Bach, J{\" o}rg-Hendrik and Caputo, Barbara
                 and Havlena, Michal and Jie, Luo and Kayser, Hendrik and Leibe, Bastian
                 and Motlicek, Petr and Pajdla, Tomas and Pavel, Misha and Torii, Akihiko
                 and Van Gool, Luc and Zweig, Alon and Hermansky, Hynek},
  title =       {The DIRAC AWEAR Audio-Visual Platform for Detection of Unexpected and
                 Incongruent Events},
  year =        {2008},
  pages =       {289-292},
  booktitle =   {ICMI 2008: Proceedings of the 10th International Conference on Multimodal
  editor =      {Vassilios Digalakis and Alexandros Potamianos and Matthew Turk and Roberto
                 Pieraccini and Yuri Ivanov},
  publisher =   {ACM},
  address =     {New York, USA},
  isbn =        {978-1-60558-198-9},
  book_pages =  {312},
  month =       {October},
  day =         {20-22},
  venue =       {Chania, Crete, Greece},
  organization ={Association for Computing Machinery},
  annote = {It is of prime importance in everyday human life to cope
    with and respond appropriately to events that are not foreseen by
    prior experience. Machines to a large extent lack the ability to
    respond appropriately to such inputs. An important class of
    unexpected events is defined by incongruent combinations of inputs
    from different modalities and therefore multimodal information
    provides a crucial cue for the identification of such events,
    e.g., the sound of a voice is being heard while the person in the
    fieldof- view does not move her lips. In the project DIRAC
    (``Detection and Identification of Rare Audio-visual Cues'') we
    have been developing algorithmic approaches to the detection of
    such events, as well as an experimental hardware platform to test
    it. An audio-visual platform ("AWEAR" - audio-visual wearable
    device) has been constructed with the goal to help users with
    disabilities or a high cognitive load to deal with unexpected
    events. Key hardware components include stereo panoramic vision
    sensors and 6-channel worn-behind-the-ear (hearing aid) microphone
    arrays. Data have been recorded to study audio-visual tracking,
    a/v scene/object classification and a/v detection of
  keywords =    {Augmented Cognition, Multimodal Interaction, 
    Audio-Visual Event Detection, Sensor Platform},
  project =     {FP6-IST-027787 DIRAC},