Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

Abstract

Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

“…Indoor occupancy is one of the important sources of information for designing smart buildings. However, challenges such as user privacy, communication limit, and a sensor’s computational capability make it difficult to develop occupancy monitoring systems [ 44 ]. Figure 10 shows the types of sensors that have been put into use by year, from 2009 to 2021.…”

FSPLO: a fast sensor placement location optimization method for cloud-aided inspection of smart buildings

“…In [ 23 ], the authors put forward a flow counting algorithm based on low resolution thermal images to avoid privacy problems in view of the possible challenges such as user privacy, communication limitation and sensor computing capability in cloud environment. At the same time, the scheme is also suitable for wireless sensor networks based on “Internet of Things” platform.…”

Efficient Deep Learning Models for Privacy-Preserving People Counting on Low-Resolution Infrared Arrays

“…In particular, we report the sensor model and resolution, its position, the target dataset, the counting algorithms, and the IoT device considered the in each work for deployment. In detail, prior works leverage both deterministic algorithms [11], [25]- [28], [30], classic ML models [32] or DL [21]- [24]. Among deterministic approaches, [11] implemented a novel real-time pattern recognition algorithm to process data sensed from doorway-mounted low-resolution IR array sensors to determine the number of people in a room.…”

“…Furthermore, multi-sensor deterministic solutions have also been explored. Specifically, [28] proposed a people flow counting algorithm to monitor occupancy in smart buildings. To achieve this goal, multiple low-resolution sensors are deployed in connection points between different building areas, in order to count the number of people moving across adjacent zones.…”