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arXiv · 1606.03713

SenseFlow: An Experimental Study for Tracking People

Kai Li·Chau Yuen·Salil S. Kanhere·Kun Hu·Wei Zhang·Fan Jiang·Xiang Liu
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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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Kai Li, Chau Yuen, Salil S. Kanhere, Kun Hu, Wei Zhang, Fan Jiang, Xiang Liu. 2016-06-12. SenseFlow: An Experimental Study for Tracking People. https://arxiv.org/abs/1606.03713

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