In the energy-efficient smart building, occupancy detection and localization is an area of increasing interest, as services, such as lighting and ventilation, could be targeted towards individual occupants instead of an entire room or floor. Also, an increasing quantity and diversity of environmental sensors are being added to smart buildings to ensure the quality of services provided by the building. The need for particulate matter (PM) sensors in consumer devices such as air purifiers, is an example where manufacturing advances have made the sensors much less expensive than laboratory equipment. Beyond their original intended use, air quality, they can also be used for occupancy monitoring. The work presented in this article proposes to use a low-cost (< 8 USD) PM sensor to infer the local movement of occupants in a corridor by sensing the resuspension of coarse (≥ 2.5 μm) particles. To obtain meaningful values from the inexpensive sensors, we have calibrated them against a laboratory-grade instrument. After calibration, we conducted a 7.8 hour experiment measuring coarse PM within a pedestrian corridor of a heavily-used office area. Comparing against ground truth data obtained by a camera, we show that the PM sensor readings are correlated with human activity, thus enabling statistical methods to infer one from the other.