  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.
