sNN-LDS: Spatio-temporal Non-negative Sparse Coding for Human Action Recognition

  • Thomas Guthier
  • Adrian Šošić
  • Volker Willert
  • Julian Eggert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


Current state-of-the-art approaches for visual human action recognition focus on complex local spatio-temporal descriptors, while the spatio-temporal relations between the descriptors are discarded. These bag-of-features (BOF) based methods come with the disadvantage of limited descriptive power, because class-specific mid- and large-scale spatio-temporal information, such as body pose sequences, cannot be represented. To overcome this restriction, we propose sparse non-negative linear dynamical systems (sNN-LDS) as a dynamic, parts-based, spatio-temporal representation of local descriptors. We provide novel learning rules based on sparse non-negative matrix factorization (sNMF) to simultaneously learn both the parts as well as their transitions. On the challenging UCF-Sports dataset our sNN-LDS combined with simple local features is competitive with state-of-the-art BOF-SVM methods.


Action Recognition Local Descriptor Sparse Code Positive Matrix Factorization Human Action Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Guthier
    • 1
  • Adrian Šošić
    • 2
  • Volker Willert
    • 1
  • Julian Eggert
    • 3
  1. 1.Control Theory and RoboticsTU DarmstadtDarmstadtGermany
  2. 2.Signal Processing GroupTU DarmstadtDarmstadtGermany
  3. 3.Honda Research Institute EuropeOffenbachGermany

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