Privacy-Preserving Understanding of Human Body Orientation for Smart Meetings

We present a method for estimating the body orientation of seated people in a smart room by fusing low-resolution range information collected from downward pointed time-of-flight (ToF) sensors with synchronized speaker identification information from microphone recordings. The ToF sensors preserve the privacy of the occupants in that they only return the range to a small set of hit points. We propose a Bayesian estimation algorithm for the quantized body orientations in which the likelihood term is based on the observed ToF data and the prior term is based on the occupants' locations and current speakers. We evaluate our algorithm in real meeting scenarios and show that it is possible to accurately estimate seated human orientation even with very low-resolution systems.