Indoor occupancy detection for commercial office spaces using sparse arrays of time-of-flight sensors

Abstract

Understanding the locations of occupants in a commercial built environment is critical for realizing energysavings by delivering lighting, heating, and cooling only where it is needed. The key to achieving this goal is being able to recognize zone occupancy in real time, without impeding occupants’ activities or compromising privacy. In this thesis, we investigate inexpensive, low-resolution time-of-flight (ToF) sensors, which can provide real-time estimates of the distances to objects in a room while preserving occupants' privacy. To estimate the number and locations of occupants with our sensors, we created both real-world and simulated testing environments and designed two generations of occupant-counting ToF systems in this environment. In the first-generation system, we designed sensor pods with VL53L1 ToF sensors and developed two algorithms: a multiple line based algorithm and a neural network method to count occupants in several specific zones. However, due to the low spatial resolution and low frame rate of our L1 pods, the first-generation system performed poorly when multiple people go through doorways at the same time. Hence, we designed a second-generation system using VL53L5 sensors. We developed and validated an algorithm for zonal occupancy counting that can deal with multiple people walking underneath the sensors in arbitrary directions. We also evaluated the system in realistic simulations of office spaces and in a real-world installation. Our system's performance was independently tested, demonstrating that it can handle moderately crowded commercial offices. While the systems have demonstrated good performance in zone counting, their performance depends on careful sensor placement. To address this issue, we propose an automatic sensor placement method that determines optimal sensor layouts for a given number of sensors and can predict the counting accuracy of such a layout. In particular, given the geometric constraints of an office environment, we simulate a large number of occupant trajectories. We then formulate the sensor placement problem as an integer linear programming (ILP) problem and solve it with the branch and bound method. To further estimate the locations of occupants, we built a human trajectory prediction model using inverse reinforcement learning. The motion of occupants was modeled as a Markov decision process (MDP) and the policy only depends on the current state and the current observation of the environment. With this model, we can estimate the distribution of occupant locations in the blind zones of our sensor system and predict their destinations.