A combined SVM/HCRF model for activity recognition based on STIPs trajectories

Mouna Selmi, Mounim A. El-Yacoubi, Bernadette Dorizzi

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

Abstract

In this paper, we propose a novel human activity recognition approach based on STIPs' trajectories as local descriptors of video sequences. This representation compares favorably with state of art feature extraction methods. In addition, we investigate the use of SVM/HCRF combination for temporal sequence modeling, where SVM is applied locally on short video segments to produce probability scores, the latter being considered as the input vectors to HCRF. This method constitutes a new contribution to the state of the art on activity recognition task. The obtained results demonstrate that our method is efficient and compares favorably with state of the art methods on human activity recognition.

Original languageEnglish
Title of host publicationICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods
Pages568-572
Number of pages5
Publication statusPublished - 27 May 2013
Externally publishedYes
Event2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013 - Barcelona, Spain
Duration: 15 Feb 201318 Feb 2013

Publication series

NameICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

Conference

Conference2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013
Country/TerritorySpain
CityBarcelona
Period15/02/1318/02/13

Keywords

  • Hidden conditional random field
  • Human activity recognition
  • SVM/HCRF combination
  • Space-time interest points' trajectories

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