Using Time-of-Flight Measurements for Privacy-Preserving Tracking in a Smart Room

We present a method for real-time person tracking and coarse pose recognition in a smart room using time-of-flight measurements. The time-of-flight images are severely downsampled to preserve the privacy of the occupants and simulate future applications that use single-pixel sensors in “smart” ceiling panels. The tracking algorithms use grayscale morphological image reconstruction to avoid false detections and are designed to not mistakenly detect pieces of furniture as people. A maximum-likelihood estimation method using a simple Markov model was implemented for robust pose classification. We show that the algorithms work effectively even when the sensors are spaced apart by 25 cm, using both real-world experiments and environmental simulation.