Human Action Recognition from Inter-Temporal Dictionaries of Key-Sequences

Human Action Recognition from Inter-Temporal Dictionaries of Key-Sequences. Alfaro, A., Mery, D., & Soto, A. In 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT, 2013.

Paper abstract bibtex 14 downloads

This paper addresses the human action recognition in video by proposing a method based on three main processing steps. First, we tackle problems related to intraclass variations and differences in video lengths. We achieve this by reducing an input video to a set of key-sequences that represent atomic meaningful acts of each action class. Second, we use sparse coding techniques to learn a representation for each key-sequence. We then join these representations still preserving information about temporal relationships. We believe that this is a key step of our approach because it provides not only a suitable shared rep resentation to characterize atomic acts, but it also encodes global tem poral consistency among these acts. Accordingly, we call this represen tation inter-temporal acts descriptor. Third, we use this representation and sparse coding techniques to classify new videos. Finally, we show that, our approach outperforms several state-of-the-art methods when is tested using common benchmarks.

@InProceedings{	  alfaro:etal:2013,
  author	= { A. Alfaro and D. Mery and A. Soto},
  title		= {Human Action Recognition from Inter-Temporal Dictionaries
		  of Key-Sequences},
  booktitle	= {6th Pacific-Rim Symposium on Image and Video Technology,
		  PSIVT},
  year		= {2013},
  abstract	= {This paper addresses the human action recognition in video
		  by proposing a method based on three main processing steps.
		  First, we tackle problems related to intraclass variations
		  and differences in video lengths. We achieve this by
		  reducing an input video to a set of key-sequences that
		  represent atomic meaningful acts of each action class.
		  Second, we use sparse coding techniques to learn a
		  representation for each key-sequence. We then join these
		  representations still preserving information about temporal
		  relationships. We believe that this is a key step of our
		  approach because it provides not only a suitable shared rep
		  resentation to characterize atomic acts, but it also
		  encodes global tem poral consistency among these acts.
		  Accordingly, we call this represen tation inter-temporal
		  acts descriptor. Third, we use this representation and
		  sparse coding techniques to classify new videos. Finally,
		  we show that, our approach outperforms several
		  state-of-the-art methods when is tested using common
		  benchmarks.},
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/Anali-PSIVT-13.pdf}
}

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