Transform based spatio-temporal descriptors for human action recognition

Ling Shao, Ruoyun Gao, Yan Liu, Hui Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

41 Citations (Scopus)

Abstract

Classic transformation methods have been widely and efficiently used in image processing areas, such as image de-noising, image segmentation, feature detection, and compression. Based on their compact signal and image representation ability, we apply the transform based techniques on the video recognition area to extract discriminative information from each given video sequence, and use the transformed coefficients as descriptors for representing and recognizing human actions in video sequences. We validate our proposed methods on the KTH and the Hollywood datasets, which have been extensively studied by a lot of researchers. The proposed descriptors, especially the wavelet transform based descriptor, yield promising results on action recognition.
Original languageEnglish
Pages (from-to)962-973
Number of pages12
JournalNeurocomputing
Volume74
Issue number6
DOIs
Publication statusPublished - 15 Feb 2011

Keywords

  • Feature extraction
  • Feature representation
  • Human action recognition
  • Spatio-temporal features
  • Transforms

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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