2018/11/27 by Irvan B. Arief-Ang, Margaret Hamilton, Flora D. Salim · 1 citation
Computer Science · Environmental Science · Medicine · Social Sciences · #Air Quality Monitoring and Forecasting #Computer science #Ecology #Human Mobility and Location-Based Analysis #Machine learning #Medicine #Occupancy #Post hoc #Scalability #Support vector machine #Video Surveillance and Tracking Methods
paper · doi:10.1145/3217214
openalex publication_date 2018/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
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 Plus Plus (DA-HOC++), a robust way to estimate the number of people within 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 occupants with minimal training data: as little as 1 day’s data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to other rooms. We evaluate DA-HOC++ with two baseline methods: a support vector regression technique and an SD-HOC model. The results demonstrate that DA-HOC++’s performance on average is better by 10.87% in comparison to SVR and 8.65% in comparison to SD-HOC.