Hierarchical dynamic parsing and encoding for action recognition

Bing Su*, Jiahuan Zhou, Xiaoqing Ding, Hao Wang, Ying Wu

*Corresponding author for this work

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

14 Scopus citations

Abstract

A video action generally exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform dynamics, this paper describes a novel hierarchical dynamic encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It provides a multi-layer joint representation for temporal modeling for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures. The dynamics within each stage are encoded by mean-pooling or learning to rank based encoding. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again to form the overall representation. Extensive experiments on a gesture action dataset (Chalearn) and several generic action datasets (Olympic Sports and Hol-lywood2) have demonstrated the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsNicu Sebe, Bastian Leibe, Max Welling, Jiri Matas
PublisherSpringer Verlag
Pages202-217
Number of pages16
ISBN (Print)9783319464923
DOIs
StatePublished - Jan 1 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9908 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Action recognition
  • Dynamic encoding
  • Hierarchical modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Su, B., Zhou, J., Ding, X., Wang, H., & Wu, Y. (2016). Hierarchical dynamic parsing and encoding for action recognition. In N. Sebe, B. Leibe, M. Welling, & J. Matas (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (pp. 202-217). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9908 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46493-0_13