Full Description

Occupancy information is important to building facility mangers in terms of predictive control, safety, as well as the indoor environment quality. Previous works have addressed different occupancy counting and estimation solutions in different buildings or spaces. In this study, we build up a test bed using the existing university lecture theatres to develop and compare three different occupancy counting methodologies. A novel CO₂ based identification model is developed using CO₂ sensor data and EnergyPlus simulation; a PTZ-Camera based face recognition algorithm and lastly a video based occupancy counting methodology. We attempt to address the occupancy counting challenge in educational building deployment scenario with large groups of people entering and leaving. Experiments have been conducted for one week with 5 minute data reporting interval. The results will be compared with the manually counted ground truth data. The results show that the PTZ-camera based face recognition has the most stable and highest accuracy with an R of 0.99; followed by the CO₂ based identification model with an R of 0.84.

Citation: 2017 Annual Conference, Long Beach, CA, Conference Papers

Product Details

Published: 2017 Number of Pages: 8Units of Measure:DualFile Size: 1 file , 750 KB Product Code(s): D-LB-17-C021