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The wavelet transform is one of the widely used transforms that proved to be very powerful in many applications, as it has strong localization ability in both frequency and space. In this paper, the 3D Stationary Wavelet Transform (SWT) is combined with a Local Binary Pattern (LBP) histogram to represent and describe the human actions in video sequences. A global representation is obtained and described using Hu invariant moments and a weighted LBP histogram is presented to describe the local structures in the wavelet representation. The directional and multi-scale information encoded in the wavelet coefficients is utilized to obtain a robust description that combine global and local descriptions in a unified feature vector. This unified vector is used to train a standard classifier. The performance of the proposed descriptor is verified using the KTH dataset and achieved high accuracy compared to existing state-of-the-art methods.

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Al-Berry, M.N., Salem, M.AM., Ebeid, H.M., Hussein, A.S., Tolba, M.F. (2016). Action Classification Using Weighted Directional Wavelet LBP Histograms. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham.

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