A new adaptive segmental matching measure for human activity recognition

Shahriar Shariat, Vladimir Pavlovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignment-based method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity measures within sequence classes and hence, raises the classification performance. We also apply a bounding technique on the histogram distances to reduce the computation of the otherwise exhaustive search.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3583-3590
Number of pages8
ISBN (Print)9781479928392
DOIs
StatePublished - 2013
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: Dec 1 2013Dec 8 2013

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Other

Other2013 14th IEEE International Conference on Computer Vision, ICCV 2013
Country/TerritoryAustralia
CitySydney, NSW
Period12/1/1312/8/13

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Keywords

  • Activity Recognition
  • Segmentation and Matching
  • Time-series alignment

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