Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect their behavior. To overcome these drawbacks, a non-intrusive technique using floor-vibration measurements, induced by human footsteps, is outlined. Detection of human-footstep impacts is an essential step to estimate the number of occupants, recognize their identities and provide an estimate of their probable locations. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. Also, signals from multiple occupants walking simultaneously overlap, which may lead to inaccurate event separation. Signals corresponding to events, once extracted, can be used to identify the number of occupants and their locations. Spurious events such as door closing, chair dragging and falling objects may produce vibrations similar to footstep-impacts. Signals from such spurious events have to be discarded as outliers to prevent inaccurate interpretations of floor vibrations for occupant detection. Walking styles differ among occupants due to their anatomies, walking speed, shoe type, health and mood. Thus, footstep-impact vibrations from the same person may vary significantly, which adds uncertainty and complicates occupant recognition. In this paper, efficient strategies for event-detection and event-signal extraction have been described. These strategies are based on variations in standard deviations over time of measured signals (using a moving window) that have been filtered to contain only low-frequency components. Methods described in this paper for event detection and event-signal extraction perform better than existing threshold-based methods (fewer false positives and false negatives). Support vector machine classifiers are used successfully to distinguish footsteps from other events and to determine the number of occupants on a floor. Convolutional neural networks help recognize the identity of occupants using footstep-induced floor vibrations. The utility of these strategies for footstep-event detection, occupant counting, and recognition is validated successfully using two full-scale case studies.