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Directional Multi-Scale Stationary Wavelet-Based Representation for Human Action Classification
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Author(s): M. N. Al-Berry (Ain Shams University, Egypt), Mohammed A.-M. Salem (Ain Shams University, Egypt), H. M. Ebeid (Ain Shams University, Egypt), A. S. Hussein (Arab Open University, Kuwait)and Mohamed F. Tolba (Ain Shams University, Egypt)
Copyright: 2017
Pages: 25
Source title:
Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch014
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Abstract
Human action recognition is a very active field in computer vision. Many important applications depend on accurate human action recognition, which is based on accurate representation of the actions. These applications include surveillance, athletic performance analysis, driver assistance, robotics, and human-centered computing. This chapter presents a thorough review of the field, concentrating the recent action representation methods that use spatio-temporal information. In addition, the authors propose a stationary wavelet-based representation of natural human actions in realistic videos. The proposed representation utilizes the 3D Stationary Wavelet Transform to encode the directional multi-scale spatio-temporal characteristics of the motion available in a frame sequence. It was tested using the Weizmann, and KTH datasets, and produced good preliminary results while having reasonable computational complexity when compared to existing state–of–the–art methods.
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