Action classification using data mining and Paris of SURF-based trajectories

Salah Alghyaline, Jun-Wei Hsieh, Hui Fen Chiang, Rui Yu Lin

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

2 Scopus citations

Abstract

A new action classification approach is proposed to improve the accuracy of the state-of-art frameworks from three folds: (1) Association rule mining is used with dense trajectories approach to discover strong relations between different visual words in the video clips, then a new histogram is built for each video clip based on such relations. (2) The second proposed approach is based on SURF descriptor to extract the most similar pairs of dense trajectories' features, and then the most similar trajectories' features are used to describe the video clip. (3) Finally, a symmetrical SURFs approach is used to detect the symmetrical pairs of trajectories in the video; the most symmetrical features in the video clip are extracted and used to describe the video clip. The above three new features are used in addition to the original dense trajectories' features for action classification. The importance of these new features is that many features are not related to the background and can significantly increase the overall recognition accuracy.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2163-2168
Number of pages6
ISBN (Electronic)9781509018970
DOIs
StatePublished - 6 Feb 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Publication series

Name2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period9/10/1612/10/16

Keywords

  • Association rule mining
  • Dense trajectories
  • Human action recognition
  • Symmetrical SURF

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