Counting Human Flow with Deep Neural Network Doong, Shing 2017-12-28T00:40:53Z 2017-12-28T00:40:53Z 2018-01-03
dc.description.abstract Human flow counting has many applications in space management. This study applied channel state information (CSI) available in IEEE 802.11n networks to characterize the flow count. Raw inputs including mean, standard deviation and five-number summary were extracted from windowed CSI data. Due to the large number of raw inputs, stacked denoising autoencoders were used to extract hierarchical features from raw inputs and a final layer of softmax regression was used to model the flow counting problem. It is found that this deep neural network structure beats other popular classification algorithms including random forest, logistic regression, support vector machine and multilayer perceptron in predicting the flow count with attractive speed performance.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2018.100
dc.identifier.isbn 978-0-9981331-1-9
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Big Data and Analytics: Pathways to Maturity
dc.subject deep neural network, machine learning, channel state information, human flow counting
dc.title Counting Human Flow with Deep Neural Network
dc.type Conference Paper
dc.type.dcmi Text
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