SenseFlow: An Experimental Study for Tracking People
June 12, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Kai Li, Chau Yuen, Salil S. Kanhere, Kun Hu, Wei Zhang, Fan Jiang, Xiang Liu
arXiv ID
1606.03713
Category
cs.HC: Human-Computer Interaction
Citations
5
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The main challenges in large-scale people tracking are the recognition of people density in a specific area and tracking the people flow path. To address these challenges, we present SenseFlow, a lightweight people tracking system. SenseFlow utilises off-the-shelf devices which sniff probe requests periodically polled by user's smartphones in a passive manner. We demonstrate the feasibility of SenseFlow by building a proof-of-concept prototype and undertaking extensive evaluations in real-world settings. We deploy the system in one laboratory to study office hours of researchers, a crowded public area in city to evaluate the scalability and performance "in the wild", and four classrooms in the university to monitor the number of students. We also evaluate SenseFlow with varying walking speeds and different models of smartphones to investigate the people flow tracking performance.
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