Occupancy Estimation from Thermal Images

TL;DR

This paper introduces a privacy-preserving occupancy estimation system using thermal images, leveraging segmentation and motion detection techniques to accurately count people in smart environments.

Contribution

It presents a novel non-intrusive method for occupancy estimation based on thermal imaging and advanced image processing techniques.

Findings

Effective occupancy estimation demonstrated on real dataset

Privacy-preserving approach suitable for smart environments

Utilizes intensity and motion-based segmentation methods

Abstract

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.