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

86 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
Pages3185-3192
Number of pages8
DOIs
StatePublished - Sep 22 2011
Event2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 - Colorado Springs, CO, United States
Duration: Jun 20 2011Jun 25 2011

Other

Other2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
CountryUnited States
CityColorado Springs, CO
Period6/20/116/25/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Wang, J., Chen, Z., & Wu, Y. (2011). Action recognition with multiscale spatio-temporal contexts. In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 (pp. 3185-3192). [5995493] https://doi.org/10.1109/CVPR.2011.5995493