Efficient object feature selection for action recognition

Tianyi Zhang, Yu Zhangg, Jianfei Cai, Alex C. Kot

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

Currently most action recognition or video classification tasks highly rely on the motion features such as state-of-the-art Improved Dense Trajectory (IDT) features. Despite the huge success, IDT features lack of rich static object-level information. In this paper, we make use of the object-level features for action recognition tasks. For efficiently and effectively processing large-scale video data, we propose a two-layer feature selection framework including local object feature selection (LS) and global feature selection (GS). Both of the selection methods can improve recognition accuracy while greatly reducing the feature dimension or feature processing complexity. Experimental results show that the selected object-level features contain complimentary information to IDT features and the combination with IDT features can further improve the recognition accuracy significantly.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
EditorsP. C. Ching, Dominic K.C. Ho
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2707-2711
Number of pages5
ISBN (Electronic)9781479999880, 9781479999873
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016
http://www.icassp2016.org/
https://ieeexplore.ieee.org/xpl/conhome/7465907/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/1625/03/16
Internet address

Keywords

  • Action Recognition
  • Feature Selection

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