Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments

Action recognition is a challenging research area in which several convolutional neural networks (CNN) based action recognition methods are recently presented. However, such methods are inefficient for real-time online data stream processing with satisfied accuracy. Therefore, in this paper we propo...

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Bibliographic Details

Published in:	Future generation computer systems Vol. 96; pp. 386 - 397
Main Authors:	Ullah, Amin, Muhammad, Khan, Haq, Ijaz Ul, Baik, Sung Wook
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.07.2019
Subjects:	Action recognition Big data processing Convolutional neural network Machine learning Non-stationary environment Online data stream analysis Optimized deep autoencoder Action recognition Convolutional neural network Machine learning Big data processing Online data stream analysis Optimized deep autoencoder Non-stationary environment
ISSN:	0167-739X, 1872-7115
Online Access:	Get full text
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