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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 4, 2020.
Abstract: In this paper, a new local feature, called, Salient Wavelet Feature with Histogram of Oriented Gradients (SWFHOG) is introduced for human action recognition and behaviour analysis. In the proposed approach, regions having maximum information are selected based on their entropies. The SWF feature descriptor is formed by using the wavelet sub-bands obtained by applying wavelet decomposition to selected regions. To improve the accuracy further, the SWF feature vector is combined with the Histogram of Oriented Gradient global feature descriptor to form the SWFHOG feature descriptor. The proposed algorithm is evaluated using publicly available KTH, Weizmann, UT Interaction, and UCF Sports datasets for action recognition. The highest accuracy of 98.33% is achieved for the UT interaction dataset. The proposed SWFHOG feature descriptor is tested for behaviour analysis to identify the actions as normal or abnormal. The actions from SBU Kinect and UT Interaction dataset are divided into two sets as Normal Behaviour and Abnormal Behaviour. For the application of behaviour analysis, 95% recognition accuracy is achieved for the SBU Kinect dataset and 97% accuracy is obtained for the UT Interaction dataset. Robustness of the proposed SWFHOG algorithm is tested against Camera view angle change and imperfect actions using Weizmann robustness testing datasets. The proposed SWFHOG method shows promising results as compared to earlier methods.
Aditi Jahagirdar and Manoj Nagmode, “A Novel Human Action Recognition and Behaviour Analysis Technique using SWFHOG” International Journal of Advanced Computer Science and Applications(IJACSA), 11(4), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110475
@article{Jahagirdar2020,
title = {A Novel Human Action Recognition and Behaviour Analysis Technique using SWFHOG},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110475},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110475},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {4},
author = {Aditi Jahagirdar and Manoj Nagmode}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.