Lighting is one of the largest power consumers in the United States and around the globe. To better understand how much energy lighting uses in a building, a lighting audit can be performed. Typically, this is a long and manual process, current solutions require significant effort on the part of the auditor. This paper develops a system using commercially available hardware and custom algorithms that enable a single human operator to quickly cover a large area while estimating light positions, type, and surface area. These tasks are accomplished with an error rate of 6.9% and 13.9%, respectively, with surface area estimation within about a factor of two.
Craig Hiller, Avideh Zakhor, "Fast, Automated Indoor Light Detection, Classification, and Measurement" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVI, 2018, pp 271-1 - 2714, https://doi.org/10.2352/ISSN.2470-1173.2018.15.COIMG-271