IS = { zkontrolovano 24 Jan 2014 },
  UPDATE  = { 2013-06-21 },
  author =      {Mik{\v s}{\'\i}k, Ond{\v r}ej and Munoz, Daniel and Bagnell, J. Andrew  and Hebert, Martial},
  title =       {Efficient Temporal Consistency for Streaming Video Scene Analysis},
  year =        {2013},
  pages =       {133-139},
  booktitle =   {ICRA2013: Proceedings of 2013 IEEE International Conference on Robotics and Automation},
  publisher =   {IEEE},
  address =     {Piscataway, USA},
  isbn =        {978-1-4673-5641-1},
issn = {1050-4729},
  book_pages =  {5865},
  month =       {May},
  day =         {6-10},
  venue =       {Karlsruhe, Germany},
  organization ={IEEE Robotics and Automation Society},
  annote =      {We address the problem of image-based scene analysis
    from streaming video, as would be seen from a moving platform, in
    order to efficiently generate spatially and temporally consistent
    predictions of semantic categories over time. In contrast to
    previous techniques which typically address this problem in batch
    and/or through graphical models, we demonstrate that by learning
    visual similarities between pixels across frames, a simple
    filtering algorithm is able to achieve high performance
    predictions in an efficient and online/causal manner. Our
    technique is a meta-algorithm that can be efficiently wrapped
    around any scene analysis technique that produces a per-pixel
    semantic category distribution.We validate our approach over three
    different scene analysis techniques on three different datasets
    that contain different semantic object categories. Our experiments
    demonstrate that our approach is very efficient in practice and
    substantially improves the consistency of the predictions over
  keywords =    {computer vision for robotics and automation, visual learning, visual navigation},
  prestige =    {important},
  note =        {CD-ROM},
  project =     {GACR P103/12/G084},
  doi =         {10.1109/ICRA.2013.6630567},
  ut_isi =      {},
  www         = {http://www.icra2013.org/},