Action recognition with multiscale spatio-temporal contexts

Jiang Wang*, Zhuoyuan Chen, Ying Wu

*Corresponding author for this work

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

89 Scopus citations

Abstract

The popular bag of words approach for action recognition is based on the classifying quantized local features density. This approach focuses excessively on the local features but discards all information about the interactions among them. Local features themselves may not be discriminative enough, but combined with their contexts, they can be very useful for the recognition of some actions. In this paper, we present a novel representation that captures contextual interactions between interest points, based on the density of all features observed in each interest point's mutliscale spatio-temporal contextual domain. We demonstrate that augmenting local features with our contextual feature significantly improves the recognition performance.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages3185-3192
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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