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Wi-Counter: Smartphone-Based People Counter Using Crowdsourced Wi-Fi Signal Data
- Source :
- IEEE Transactions on Human-Machine Systems. 45:442-452
- Publication Year :
- 2015
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2015.
-
Abstract
- Reliable people counting is crucial to many urban applications. However, most existing people counting systems are sensor-based and can only work in some fixed gateways or checkpoints where sensors have been installed. This high dependence on the exact locations of sensors leads to low accuracy. To overcome these limitations, in this paper, we propose a smartphone-based people counting system, Wi-Counter, by leveraging the pervasive Wi-Fi infrastructure. To collect comprehensive Wi-Fi signals and people count information based on crowdsource, Wi-Counter first adopts a preprocessor to overcome the noisy, discrepant, and fragile data based on the Wiener filter and Newton interpolation. It then makes use of the designated five-layer neural network to learn the relation model between the Wi-Fi signals and the number of people. By analyzing the received Wi-Fi signals, Wi-Counter can estimate the number of people based on the resulting model. We have conducted experiments by implementing a prototype of Wi-counter based on smartphones and evaluated the system in terms of accuracy and power consumption in an indoor testbed covering an area of 96 m $^2$ . Wi-Counter achieved a counting accuracy of up to 93% and exhibited reliable and robust performance resisting temporal environmental changes with negligible power usage.
- Subjects :
- Artificial neural network
Relation (database)
Noise measurement
Computer Networks and Communications
Computer science
business.industry
Testbed
Wiener filter
Real-time computing
Human Factors and Ergonomics
Computer Science Applications
Human-Computer Interaction
symbols.namesake
Artificial Intelligence
Control and Systems Engineering
Embedded system
Signal Processing
symbols
People counter
Preprocessor
business
Interpolation
Subjects
Details
- ISSN :
- 21682305 and 21682291
- Volume :
- 45
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Human-Machine Systems
- Accession number :
- edsair.doi...........49070a3df6768d42dcf7063c155bb561