Action Recognition using Key-Frame Features of Depth Sequence and ELM
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 10
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.
Authors and Affiliations
Suolan Liu, Hongyuan Wang
Keywords
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- EP ID EP260744
- DOI 10.14569/IJACSA.2017.081007
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