Group activity recognition based on ARMA shape sequence modeling

Ying Wang*, Kaiqi Huang, Tieniu Tan

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

In this paper, we propose a system identification approach for group activity recognition in traffic surveillance. Statistical shape theory is used to extract features, and then ARMA (Autoregressive and Moving Average) is adopted for feature learning and activity identification. Here only a few points, instead of the complete trajectory of each object are used to describe the dynamic information of group activity. And ARMA is employed to learn activity sequences. The performance of the proposed method is proved by experiments on 570 video sequences, with the average recognition rate of 88% (compared with 81% of HMM). The extracted features are invariant to zoom, pan and tilt, which is also proved in the experiments.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Image Processing
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages209-212
Number of pages4
Volume3
ISBN (Print)1424414377, 9781424414376
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: 16 Sep 200719 Sep 2007

Publication series

NameInternational Conference on Image Processing Proceedings
PublisherIEEE
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period16/09/0719/09/07

Keywords

  • ARMA
  • Group activity
  • Landmark
  • Shape theory
  • Surveillance

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