Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models

Abstract

In traditional vision systems, high level information is usually inferred from images or videos captured by cameras, or depth images captured by depth sensors. These images, whether gray-level, RGB, or depth, have a human-readable 2D structure which describes the spatial distribution of the scene. In this paper, we explore the possibility to use distributed color sensors to infer high level information, such as room occupancy. Unlike a camera, the output of a color sensor has only a few variables. However, if the light in the room is color controllable, we can use the outputs of multiple color sensors under different lighting conditions to recover the light transport model (LTM) in the room. While the room occupancy changes, the LTM also changes accordingly, and we can use machine learning to establish the mapping from LTM to room occupancy.