Occupancy Estimation from Thermal Images
This addresses occupancy estimation for smart environments with a privacy-preserving approach, but it appears incremental as it builds on existing segmentation concepts.
The researchers tackled occupancy estimation in smart environments by developing a system that uses thermal images and intensity/motion-based human segmentation, achieving effectiveness demonstrated on a real dataset.
We propose a non-intrusive, and privacy-preserving occupancy estimation system for smart environments. The proposed scheme uses thermal images to detect the number of people in a given area. The occupancy estimation model is designed using the concepts of intensity-based and motion-based human segmentation. The notion of difference catcher, connected component labeling, noise filter, and memory propagation are utilized to estimate the occupancy number. We use a real dataset to demonstrate the effectiveness of the proposed system.