Motion of Oriented Magnitudes Patterns for Human Action Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Hai-Hong Phan , Ngoc-Son Vu , Vu-Lam Nguyen , Mathias Quoy

ABSTRACT

In this paper, we present a novel descriptor for human action recognition, called Motion of Oriented Magnitudes Patterns (MOMP), which considers the relationships between the local gradient distributions of neighboring patches coming from successive frames in video. The proposed descriptor also characterizes the information changing across different orientations, is therefore very discriminative and robust. The major advantages of MOMP are its very fast computation time and simple implementation. Subsequently, our features are combined with an effective coding scheme VLAD (Vector of locally aggregated descriptors) in the feature representation step, and a SVM (Support Vector Machine) classifier in order to better represent and classify the actions. By experimenting on several common benchmarks, we obtain the state-of-the-art results on the KTH dataset as well as the performance comparable to the literature on the UCF Sport dataset. More... »

PAGES

168-177

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010. Convolutional Learning of Spatio-temporal Features in COMPUTER VISION – ECCV 2010
  • 2010. Face Recognition with Patterns of Oriented Edge Magnitudes in COMPUTER VISION – ECCV 2010
  • 2006. Human Detection Using Oriented Histograms of Flow and Appearance in COMPUTER VISION – ECCV 2006
  • 2013-05. Dense Trajectories and Motion Boundary Descriptors for Action Recognition in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2009. Group Action Recognition Using Space-Time Interest Points in ADVANCES IN VISUAL COMPUTING
  • 2012. Motion Interchange Patterns for Action Recognition in Unconstrained Videos in COMPUTER VISION – ECCV 2012
  • 2005-09. On Space-Time Interest Points in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Advances in Visual Computing

    ISBN

    978-3-319-50831-3
    978-3-319-50832-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-50832-0_17

    DOI

    http://dx.doi.org/10.1007/978-3-319-50832-0_17

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045390213


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