Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Abstract

Using passive infrared sensors is a well-established technique of presence monitoring. While it can significantly reduce energy consumption, more savings can be made when utilising more modern sensor solutions coupled with machine learning algorithms. This paper proposes an improved method of presence monitoring, which can accurately derive the number of people in the area supervised with a low-cost and low-energy thermal imaging sensor. The method utilises U-Net-like convolutional neural network architecture and has a low parameter count, and therefore can be used in embedded scenarios. Instead of providing simple, binary information, it learns to estimate the occupancy density function with the person count and approximate location, allowing the system to become considerably more flexible. The tests show that the method compares favourably to the state of the art solutions, achieving significantly better results.

Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware

“…The presented work extends research conducted in [27] with examinations related to algorithm optimization and quantization for edge and embedded applications using various methods, frameworks, and tools. In addition, validation, performance tests, and comparison of low-cost hardware with resource limitations are significant parts of this study as in [26], [29].…”

“…This research uses the Thermo Presence dataset published as part of a [27] research paper. Thermal images were collected using a single-board computer -Raspberry Pi 4.…”

“…Every frame in the dataset incorporates information about a person's center X and Y coordinates. Like the authors of [27], the ground truth mask is constructed by setting a single pixel, corresponding to an individual location, to a maximum value. Subsequently, the occupancy density map was created using the convolution of the above-mentioned image with a 2D Gaussian mask (σ = 3).…”

“…The metrics obtained compared to other state-of-the-art solutions. Mean absolute error, mean square error, and their rounded version (in the scope of people count) for direct comparison with [26] and [27]. In IoT applications, devices spend most of the time in a sleep mode waiting for a cyclic interrupt to trigger their action.…”

“…This solution has a significant drawback in the form of an upper range limit, which disallows reading the higher value from the frame than a number of classes provided during a training phase. Kraft et al [27] introduced a method based on density estimation. This concept is bor-rowed from the crowd counting task [28], which is typically implemented using an RGB sensor.…”