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Directional Stationary Wavelet-Based Representation for Human Action Classification

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Advanced Machine Learning Technologies and Applications (AMLTA 2014)

Abstract

This paper proposes a directional wavelet-based representation of natural human actions in realistic videos. This task is very important for human action recognition, which has become one of the most important fields in computer vision. Its importance comes from the large number of applications that employ human action classification and recognition. The proposed method utilizes the 3D Stationary Wavelet Analysis to encode the directional spatio-temporal characteristics of the motion available in video sequences. It was tested using the Weizmann dataset, and produced promising preliminary results (92.47 % classification accuracy) when compared to existing state–of–the–art methods.

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© 2014 Springer International Publishing Switzerland

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Al-Berry, M.N., Salem, M.A.M., Ebeid, H.M., Hussein, A.S., Tolba, M.F. (2014). Directional Stationary Wavelet-Based Representation for Human Action Classification. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-13461-1_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13460-4

  • Online ISBN: 978-3-319-13461-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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