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Paper in IEEE CVPR 2013 “Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition”

  • V. Bettadapura, G. Schindler, T. Ploetz, and I. Essa (2013), “Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. [PDF] [WEBSITE] [DOI] [BIBTEX]
    @inproceedings{2013-Bettadapura-ABDDTSIAR,
      Author = {Vinay Bettadapura and Grant Schindler and Thomas Ploetz and Irfan Essa},
      Booktitle = {{Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
      Date-Added = {2013-06-25 11:42:31 +0000},
      Date-Modified = {2014-04-28 17:10:00 +0000},
      Doi = {10.1109/CVPR.2013.338},
      Month = {June},
      Organization = {IEEE Computer Society},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Bettadapura-ABDDTSIAR.pdf},
      Title = {Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition},
      Url = {http://www.cc.gatech.edu/cpl/projects/abow/},
      Year = {2013},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/abow/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/CVPR.2013.338}}

Abstract

We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams. In addition, we also propose the use of randomly sampled regular expressions to discover and encode patterns in activities. We demonstrate the effectiveness of our approach in experimental evaluations where we successfully recognize activities and detect anomalies in four complex datasets.

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