Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways

Abstract

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C.

IndoorCare: Low-Cost Elderly Activity Monitoring System through Image Processing

“…In ref. [30], the authors used infrared (IR) sensors to calculate the number of people inside a building, installing sensors on the doors' tops to detect transit movement between rooms, so they could calculate how many people were in each division.…”

“…Case Study Why Was Chosen [23] Hand gesture detection Image data extraction using OpenCV [24] Video processing on Raspberry PI OpenCV on Raspberry PI [25] Human facial recognition OpenCV on Raspberry PI [27] Extraction of vehicle information OpenCV on Raspberry PI [28] Surveillance system Image acquisition on Raspberry PI [29] Indoor/outdoor person detection Uses RFID tags to detect humans [30] Indoor human detection Uses infrared sensor to detect humans [31] Indoor monitoring Uses IoT devices with AI IndoorCare Indoor human monitoring Uses IoT devices with Computer Vision…”

“…The solution IndoorCare, proposed in this article, is based on some principles used in other solutions, namely using only one device per room for human detection [29], detecting the presence of people through motion capture analysis [30], and using low-cost microcomputers to analyze and process the collected information [31]. The system has a distributed and multi-agent architecture [32], which implements the client-server model, having a gateway module to ensure information security, as presented in Figure 1.…”

An Improved IoT-Based System for Detecting the Number of People and Their Distribution in a Classroom

“…The authors proposed, implemented, and evaluated their system and demonstrated that the proposed magnetic field-based system is much more reliable than previously proposed Bluetooth-based approaches. Another possible solution for monitoring people in indoor environments was introduced by Perra et al [ 34 ]. The proposed device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor.…”

Multi Sensor-Based Obstacle Avoidance Algorithm in Visual Engineering Environment

“…Though its range is much shorter than sound-based ranging sensor, infrared sensor triumphs in resolution and higher distance measurement rate [18]- [19]. Using a group of infrared sensors can provide an effective full coverage of the robot's path at a cheaper price than laser-based and radarbased sensors [20]- [21].…”