Non-parametric Bayesian Learning for Newcomer Detection using Footstep-Induced Floor Vibration: Poster Abstract

Detecting a previously unknown person (newcomer detection) is critical for visitor management, intruder prevention, and access control in smart buildings. Biometrics have been used to detect newcomers, including face, fingerprint, voice, iris, etc. These approaches often require active participation of the users or require dense instrumentation. Prior work using footstep-induced floor vibration to identify people removes these requirements, but only functions for known people due to the high variability in footstep-induced vibrations and the limited number of predicted classes in supervised learning. To overcome the limitations, we introduce a newcomer detection system based on non-parametric Bayesian learning, which models the variability and distribution of consecutive footstep-induced floor vibration for newcomer walking patterns. Preliminary results from real-world experiments with 6 people show up to 92% accuracy in newcomer detection with an average of 4 consecutive footsteps.