Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/49987

Counting Human Flow with Deep Neural Network

File Size Format  
paper0100.pdf 433 kB Adobe PDF View/Open

Item Summary

Title:Counting Human Flow with Deep Neural Network
Authors:Doong, Shing
Keywords:Big Data and Analytics: Pathways to Maturity
deep neural network, machine learning, channel state information, human flow counting
Date Issued:03 Jan 2018
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.
Pages/Duration:10 pages
URI/DOI:http://hdl.handle.net/10125/49987
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.100
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data and Analytics: Pathways to Maturity


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons