Deep-Learning-Based Occupant Counting by Ambient RF Sensing

Sharma, Pragya; Xu, Guoyi; Hui, Xiaonan; Hysell, D. L.; Kan, Edwin C.

doi:10.1109/jsen.2020.3045035

Cited by 11 publications

(5 citation statements)

References 52 publications

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“…The use of wireless communication technology to estimate occupancy counts is mainly through Wi-Fi, Bluetooth, BLE, RFID, and so on [44]. In recent years, with the continuous improvement of Wi-Fi infrastructure applications and ubiquitous Wi-Fi-enabled mobile devices, it has become possible to use COTS Wi-Fi routers and mobile devices carried by passengers for occupancy detection.…”

Section: Wireless Communicationmentioning

confidence: 99%

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A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021

Zhao

Liang

et al. 2022

Electronics

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Occupancy information is important to building facility managers in terms of building energy efficiency, indoor environmental quality, comfort conditions, and safety management of buildings. When combing the distribution characteristics of the literature, it is found that the field of estimating occupancy counts is a very active area. Researchers from various countries have undertaken extensive explorations to obtain more research results. In this survey, the commonly used occupancy measurement systems and algorithms are described. Through the analysis and research of different occupancy measurement systems and algorithms, their advantages, disadvantages, and limitations are summarized, so that researchers can use them selectively. As for how to choose the method of estimating occupancy counts, suggestions are given in terms of the range of people, accuracy, cost, and privacy. There are still many pressing issues relating to high-density crowd occupancy counting, complex environmental impact, and system robustness. According to the current research progress and technology development trend, the possible future research directions are pointed out. The innovation of this review is the quantitative analysis of the selection of occupancy measurement systems for different ranges of people, and the occupancy counting accuracy situation of different measurement systems and algorithms. It provides more informed opinions on the selection of practical applications. It can be used by other researchers as a starting point for their research and/or project work.

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Section: Wireless Communicationmentioning

confidence: 99%

“…Many authors have researched the robustness of the system. Sharma et al [44] have explored using the same CNN model in a different home setting with a single occupant. High counting accuracy can still be maintained under placement variations of tags, receivers, and furnishing.…”

Section: Challenges Of System Robustnessmentioning

confidence: 99%

A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021

Zhao

Liang

et al. 2022

Electronics

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“…Considerably, many deep learning methodologies for image and environmental sensorbased occupancy estimation research showed promising results [11,[28][29][30][31][32][33][34][35]. A people counting algorithm on thermal images-based on CNN was developed by Gomez et.al.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, an indoor occupancy counting algorithm based on the CNN model which learns features from a received signal strength indicator (RSSI) and phase data were proposed by Sharma et. al [30]. The performance of the model was 0.82 probability for detecting the correct number of occupants, and 1.0 if ±1 error was permitted.…”

Section: Related Workmentioning

confidence: 99%

Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy

et al. 2021

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The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.

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“…Internet of Things (IoT) continues to drive smart homes, smart cities, smart manufacturing, and digital agriculture with prolific RF transceivers [13], [14], [15], many requiring accurate phase measurements of channel propagation. These include high-precision detection and location [16], [17], [18], [19], tracking and mapping [20], [21], and monitoring of vital signs [22], [23]. When building large-scale multi-static systems using certain commercial products, e.g., software defined radio (SDR) platforms or RF system-on-chip (SoC) [24], non-ideal phase offsets need to be calibrated over channels without shared PLL.…”

Section: Introductionmentioning

confidence: 99%

Phase Offset Calibration in Multi-Channel Radio-Frequency Transceivers

Xu,

Kan

2024

IEEE J. Microw.

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Phase measurements by the quadrature scheme in radio transceivers can be applied to phase-sensitive applications like precision multi-static 3D localization. However, measurements at different channels with individual local oscillators suffer from random phase offsets due to non-repeatable initial phases of phase-locked loops in frequency synthesizers. In this paper, a novel phase calibration method is proposed to cancel out both the random and systematic time-invariant phase offsets at the superheterodyne receiver frontends. Direct phase offset measurements and on-site calibration are made possible by additional hardware connections, achieving simple implementation and accurate differential phase measurements without relying on bandwidth resources. The proposed calibration method generates repeatable phases for each device reboot with standard deviation less than 2 degrees, which translates to 0.9 mm ranging accuracy for 1.8 GHz carrier frequency. This method can also be flexibly extended to accommodate a broad range of practical network scenarios with more channels, various network topologies, and a wider bandwidth.INDEX TERMS Multi-channel radio transceivers, phase calibration, phase locked loops (PLL), radio frequency (RF), software defined radios (SDR).

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Deep-Learning-Based Occupant Counting by Ambient RF Sensing

Cited by 11 publications

References 52 publications

A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021

A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021

Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy

Phase Offset Calibration in Multi-Channel Radio-Frequency Transceivers

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