Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Abstract

We present a method for real-time person tracking and coarse pose estimation in a smart room using a sparse array of single pixel time-of flight (ToF) sensors mounted in the ceiling of the room. The single pixel sensors are relatively inexpensive compared to commercial ToF cameras and are privacy preserving in that they only return the range to a small set of hit points. The tracking algorithm includes higher level logic about how people move and interact in a room and makes estimates about the locations of people even in the absence of direct measurements. A maximum likelihood classifier based on features extracted from the time series of ToF measurements is used for robust pose classification into sitting, standing and walking states. We use both computer simulation and real-world experiments to show that the algorithms are capable of robust person tracking and pose estimation even with a sensor spacing of 60 cm (i.e., 1 sensor per ceiling tile).