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Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.081007
Article Published in 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