Real-Time Occupancy Detection System Using Low-Resolution Thermopile Array Sensor for Indoor Environment

Abstract

Low-Resolution Thermopile Array Sensors are widely used in several indoor applications such as security, intelligent surveillance, robotics, military, and health monitoring systems. It is compact, cost-effective, and offers a low-resolution thermal image of the environment, attracting its use in privacy-focused applications. Many industries migrating towards Industry 4.0 are facing challenges in using sensors and automating the systems. One of the areas in which automation could be implemented is by using sensors to operate the systems smartly based on occupancy. The major challenge in such applications is maintaining privacy; conventional imaging mechanisms using optical camera systems fail to achieve it. The same could be achieved by using thermopile sensors which provide thermal data of the desired region. This generates the possibility to identify the number of people in a specified area without revealing their identity. This paper proposes various approaches to detect human occupancy using a low-resolution infrared thermopile array sensor to keep their identity safe and avoid privacy issues. The proposed system detects IR-emitting objects using a low-resolution thermopile array Grid-EYE sensor (AMG8833). The sensor acquires $8\times 8$ pixels of thermal distribution. These thermal distribution data are subjected to interpolation, filtering, adaptive thresholding, and background suppression to attain the set goal of human detection.

Keywords

• Thermopile array sensor
• human target detection
• bicubic interpolation
• Gaussian filter
• adaptive threshold
• Raspberry Pi