Human action recognition in stereoscopic videos based on bag of features and disparity pyramids

Alexandros Iosifidis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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In this paper, we propose a method for human action recognition in unconstrained environments based on stereoscopic videos. We describe a video representation scheme that exploits the enriched visual and disparity information that is available for such data. Each stereoscopic video is represented by multiple vectors, evaluated on video locations corresponding to different disparity zones. By using these vectors, multiple action descriptions can be determined that either correspond to specific disparity zones, or combine information appearing in different disparity zones in the classification phase. Experimental results denote that the proposed approach enhances action classification performance, when compared to the standard approach, and achieves state-of-the-art performance on the Hollywood 3D database designed for the recognition of complex actions in unconstrained environments.
Original languageEnglish
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO 2014)
Subtitle of host publicationProceedings of a meeting held 1-5 September 2014, Lisbon, Portugal
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9780992862619
ISBN (Print)9781479946037
Publication statusPublished - Jan 2015
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, United Kingdom
Duration: 1 Sep 20145 Sep 2014

Publication series

NameProceedings of the European Signal Processing Conference (EUSIPCO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)2219-5491


Conference22nd European Signal Processing Conference, EUSIPCO 2014
Country/TerritoryUnited Kingdom


  • Human Action Recognition
  • Stereoscopic Videos
  • Disparity Pyramids
  • Bag of Features

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