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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 10, 2017.
Abstract: Recently, the rapid development of inexpensive RGB-D sensor, like Microsoft Kinect, provides adequate information for human action recognition. In this paper, a recognition algorithm is presented in which feature representation is generated by concatenating spatial features from human contour of key frames and temporal features from time difference information of a sequence. Then, an improved multi-hidden layers extreme learning machine is introduced as classifier. At last, we test our scheme on the public UTD-MHAD dataset from recognition accuracy and time consumption.
Suolan Liu and Hongyuan Wang, “Action Recognition using Key-Frame Features of Depth Sequence and ELM” International Journal of Advanced Computer Science and Applications(IJACSA), 8(10), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081007
@article{Liu2017,
title = {Action Recognition using Key-Frame Features of Depth Sequence and ELM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081007},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081007},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {10},
author = {Suolan Liu and Hongyuan Wang}
}
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.