Occupancy Estimation from Thermal Images
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.
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