2017/11/08 by Irvan B. Arief-Ang, Flora D. Salim, Margaret Hamilton · 3 citations
Computer Science · Engineering · Environmental Science · Mathematics · Medicine · Social Sciences · #Air Quality Monitoring and Forecasting #Artificial intelligence #Computer science #Engineering #Human Mobility and Location-Based Analysis #Mathematics #Medicine #Occupancy #Post hoc #Post-hoc analysis #Statistics #Support vector machine #Video Surveillance and Tracking Methods #Wireless ad hoc network
paper · doi:10.1145/3137133.3137146
openalex publication_date 2017/11/08 · openalex created_date 2018/04/24 · openalex updated_date 2026/07/29
Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter (DA-HOC), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC is able to predict the number of occupancy with minimal training data, as little as one-day data. DA-HOC accurately predicts indoor human occupancy for a large room using a model trained from a small room and adapted to the larger room. We evaluate DA-HOC with two baseline methods - support vector regression technique and SD-HOC model. The results demonstrate that DA-HOC's performance is better by 12.29% in comparison to SVR and 10.14% in comparison to SD-HOC.