In sensed buildings, information related to occupant movement helps optimize important func-tionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. These systems compromise the privacy of building occupants and may affect their behavior. Occupant detection and tracking using floor-vibration measurements that are induced by footsteps is a non-intrusive and inex-pensive sensing method. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. In addition, spurious events such as door closing and falling objects may produce vibrations that are similar to footstep impacts. These events have to be detected and disregarded. Tracking occupants is complicated due to uncertainties associated with walking styles, walking speed, shoe type, health, and mood. Also, spatial variation in structural behavior of floor slabs adds ambiguity to the task of occupant tracking, which cannot be addressed using data-driven strategies alone. In this paper, a frame-work for occupant detection and tracking is developed. Occupant detection is carried out based on signal information. This method outperforms existing threshold-based methods. Support- vector-machine classifiers, trained with time and frequency-domain features, successfully distinguish footsteps from spurious events and determine the number of occupants walking simultaneously. A model-based data-interpretation approach is used for occupant tracking. Structural-mechanics models are used to identify a population of possible occupant locations and trajectories. Up to two occupants can be tracked by accommodating systematic bias and un-certainties from sources such as modeling assumptions and variability in walking gaits. A hybrid framework for occupant detection and tracking that combines model-free approaches for occu-pancy detection with structural behavior models for tracking is developed and tested on two full- scale case studies. These studies successfully validate the utility of the framework for buildings having sparse sensor configurations that measure floor vibrations.