Natural action recognition using invariant 3D motion encoding (bibtex)
by Simon Hadfield, Karel Lebeda, Richard Bowden
Abstract:
We investigate the recognition of actions "in the wild"' using 3D motion information. The lack of control over (and knowledge of) the camera configuration, exacerbates this already challenging task, by introducing systematic projective inconsistencies between 3D motion fields, hugely increasing intra-class variance. By introducing a robust, sequence based, stereo calibration technique, we reduce these inconsistencies from fully projective to a simple similarity transform. We then introduce motion encoding techniques which provide the necessary scale invariance, along with additional invariances to changes in camera viewpoint. On the recent Hollywood 3D natural action recognition dataset, we show improvements of 40% over previous state-of-the-art techniques based on implicit motion encoding. We also demonstrate that our robust sequence calibration simplifies the task of recognising actions, leading to recognition rates 2.5 times those for the same technique without calibration. In addition, the sequence calibrations are made available.
Reference:
Natural action recognition using invariant 3D motion encoding (Simon Hadfield, Karel Lebeda, Richard Bowden), In Proceedings of the European Conference on Computer Vision (ECCV) (Tinne Tuytelaars, Bernt Schiele, Tomas Pajdla, David Fleet, eds.), Springer, volume 8690, 2014. (Poster, Spotlight Video)
Bibtex Entry:
@InProceedings{Hadfield14b,
  Title                    = {Natural action recognition using invariant 3D motion encoding},
  Author                   = {Simon Hadfield and Karel Lebeda and Richard Bowden},
  Booktitle                = {Proceedings of the European Conference on Computer Vision (ECCV)},
  Year                     = {2014},

  Address                  = {Zurich, Switzerland},
  Editor                   = {Tinne Tuytelaars and Bernt Schiele and Tomas Pajdla and David Fleet},
  Month                    = {6 -- 12 } # sep,
  Pages                    = {758 -- 771},
  Publisher                = {Springer},
  Series                   = {Lecture Notes in Computer Science},
  Volume                   = {8690},

  Abstract                 = {We investigate the recognition of actions "in the wild"' using 3D motion information. The lack of control over (and knowledge of) the camera configuration, exacerbates this already challenging task, by introducing systematic projective inconsistencies between 3D motion fields, hugely increasing intra-class variance. By introducing a robust, sequence based, stereo calibration technique, we reduce these inconsistencies from fully projective to a simple similarity transform. We then introduce motion encoding techniques which provide the necessary scale invariance, along with additional invariances to changes in camera viewpoint. On the recent Hollywood 3D natural action recognition dataset, we show improvements of 40% over previous state-of-the-art techniques based on implicit motion encoding. We also demonstrate that our robust sequence calibration simplifies the task of recognising actions, leading to recognition rates 2.5 times those for the same technique without calibration. In addition, the sequence calibrations are made available.},
  Comment                  = {<a href="http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/posters/Natural%20action%20recognition%20using%20invariant%203D%20motion%20encoding.tif">Poster</a>, <a href="http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/videos/Natural%20action%20recognition%20using%20invariant%203D%20motion%20encoding.mp4">Spotlight Video</a>},
  Crossref                 = {ECCV14},
  Doi                      = {10.1007/978-3-319-10605-2_49},
  Gsid                     = {631356999399107210},
  Keywords                 = {Action recognition, in the wild, 3D motion, scene flow, invariant encoding, stereo sequence calibration},
  Timestamp                = {2014.06.19},
  Url                      = {http://personal.ee.surrey.ac.uk/Personal/S.Hadfield/papers/Natural%20action%20recognition%20using%20invariant%203D%20motion%20encoding.pdf}
}
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