Home automation systems should evolve to the next phase in which they are aware of contexts, because providing services based on contexts will upgrade the service quality, thus making people more comfortable and home living more convenient. In particular, location is a piece of important and useful context information for seeking appropriate services, as well as providing them to people living in a home. So far, there have been several studies focused on tracking inhabitants in smart home environments. However, these approaches are often intrusive or require inhabitants to wear some sort of devices, which may make the inhabitants uncomfortable or even inconvenient. This problem could devastate the ultimate goal, which is to provide convenient services, and hence cause such approaches somewhat controversial. In this study, we utilize a number of load sensors together to construct a sensory floor on which exerted pressure can be detected and cover its surface with wooden flooring as in a normal home environment. Although the wooden flooring provides a flat surface for inhabitants to walk on, it also causes clutter in the sensor readings, which lead to difficulty in clearly identifying the location(s) of pressure source(s). Thus, we apply probabilistic data association technique and LeZi-Update approach to analyze the cluttered pressure readings collected by the load sensors so as to determine the positions of the inhabitants, as well as to track their movements. With our nonintrusive approach, there is no need for inhabitants to wear any devices, and this also solves the cross-walking problem in the home environment. Note to Practitioners-This system aims for detecting the location of multiple inhabitants in the home environment. We adopt the sensory floor as our tracking sensor, which consists of many blocks, each with a load cell to collect the human body weight. These blocks are covered with conventional wooden flooring to provide a flat surface on which inhabitant- s can walk freely. After collecting data, we apply mathematical techniques to analyze them, thus determining inhabitants' locations and tracking their movements. The limitation is that the system cannot differentiate different persons if they have close weight readings, which is the natural limitation of the load cell.