Conditional Random Fields based approach for real time building occupancy estimation with multi sensory networks
Abstract Automated real time occupancy monitoring in buildings plays an important role in increasing energy efficiency and provides facility managers with useful information about the usage of different spaces. In this paper, a novel approach is proposed for estimating real time occupancy in buildings, based on Conditional Random Field probabilistic models, utilizing data from different sensor types. Three different types of occupancy information are considered: presence/absence, actual number of occupants and occupancy density. The proposed occupancy estimation method has been applied to four spaces with different characteristics in a real life testbed environment. Experimental results revealed that the proposed method yielded high accuracy for different sensor combinations in all tested configurations regarding the occupancy granularity and the space type, and outperformed the Hidden Markov Model based method.