Autonomous Human Flow Counting Service With Deep Neural Network

Shing Hwang Doong
Additional contact information
Shing Hwang Doong: Shu-Te University, Kaohsiung, Taiwan

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2018, vol. 8, issue 3, 18-36

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 features including the mean, standard deviation and five-number summary were extracted from CSI magnitudes in a time window. Due to the large number of raw features, stacked denoising autoencoders were used to extract higher level features and a final layer of softmax regression was used to classify the flow count. The resulting neural network beat many popular classification algorithms in predicting the correct flow size. In addition to CSI magnitudes, this study also explored the feasibility of using CSI phase-based features. It is found that the magnitude neural network provided a better prediction result than the phase neural network, and combining both networks yielded an even better solution to the flow counting problem.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJSSOE.2018070102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jssoe0:v:8:y:2018:i:3:p:18-36

Access Statistics for this article

International Journal of Systems and Service-Oriented Engineering (IJSSOE) is currently edited by Wuhui Chen

More articles in International Journal of Systems and Service-Oriented Engineering (IJSSOE) from IGI Global
Bibliographic data for series maintained by Journal Editor ().