SenseFlow: An Experimental Study for Tracking People
This work addresses people tracking challenges for applications such as monitoring office hours, public crowd flow, and student attendance, but it appears incremental as it builds on existing passive sensing methods.
The authors tackled the problem of large-scale people tracking by developing SenseFlow, a lightweight system that uses off-the-shelf devices to passively sniff smartphone probe requests, and they demonstrated its feasibility through real-world evaluations in settings like a laboratory, public area, and classrooms.
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.