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A structured multi-feature representation for recognizing human action and interaction

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Active research has been carried out for human action recognition using 3D human skeleton joints with the release of cost-efficient RGB-D sensors. However, extracting discriminative features from noisy skeleton sequences to effectively distinguish various human action or interaction categories still remains challenging. This paper proposes a structured multi-feature representation for human action and interaction recognition. Specifically, a novel kernel enhanced bag of semantic words (BSW) is designed to represent the dynamic property of skeleton trajectories. By aggregating BSW with the geometric feature, a GBSW representation is constructed for human action recognition. For human interaction recognition where the cooperation of each subject matters, a GBSWC representation is proposed via combining the GBSW feature with a correlation feature which addresses the intrinsic relationship between interactive persons. Experimental results on several human action and interaction datasets demonstrate the superior performances of the proposed features over the state-of-the-art methods.
Original languageEnglish
JournalNeurocomputing
Early online date31 Aug 2018
DOIs
Publication statusEarly online - 31 Aug 2018

Documents

  • neurocomputing_pure

    Accepted author manuscript (Post-print), 3.27 MB, PDF document

    Licence: CC BY-NC-ND

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