CS-light: Camera sensing based occupancy-aware robust smart building lighting control - RAVI, Anuradha (Institutional Knowledge at Singapore Management University, 2021)
We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) of image frames into accurate estimates of the illuminance (LUX) of the various sub-regions, under variations in ambient lighting and layouts. To overcome this challenge, we develop a novel technique that (a) classifies image pixels as corresponding to light vs. dark-colored surfaces, and (b) uses unsupervised ML-based color-specific, pixel-to-LUX classifiers and statistical aggregation to provide robust LUX estimates. Experimental studies, conducted over a collaborative work area in an operational ZEB, demonstrate CS-Light's efficacy: it supports accurate pixel-to-LUX estimation (median error= 8.5%), and its real-time multi-LED adaptation results in appreciable energy savings (63.5% in low occupancy situations), while ensuring negligible perceptual discomfort to human occupants.