Mining discriminative 3D Poselet for cross-view action recognition

Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu

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

2 Scopus citations

Abstract

This paper presents a novel approach to cross-view action recognition. Traditional cross-view action recognition methods typically rely on local appearance/motion features. In this paper, we take advantage of the recent developments of depth cameras to build a more discriminative cross-view action representation. In this representation, an action is characterized by the spatio-temporal configuration of 3D Poselets, which are discriminatively discovered with a novel Poselet mining algorithm and can be detected with view-invariant 3D Poselet detectors. The Kinect skeleton is employed to facilitate the 3D Poselet mining and 3D Poselet detectors learning, but the recognition is solely based on 2D video input. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition.

Original languageEnglish (US)
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PublisherIEEE Computer Society
Pages634-639
Number of pages6
ISBN (Print)9781479949854
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States
Duration: Mar 24 2014Mar 26 2014

Publication series

Name2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Other

Other2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
CountryUnited States
CitySteamboat Springs, CO
Period3/24/143/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Wang, J., Nie, X., Xia, Y., & Wu, Y. (2014). Mining discriminative 3D Poselet for cross-view action recognition. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 634-639). [6836043] (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014). IEEE Computer Society. https://doi.org/10.1109/WACV.2014.6836043