Device-Free Crowd Counting Based on the Phase Difference of Channel State Information

Zong, Jing; Huang, Binke; He, Liang; Yang, Bo; Cheng, Xiaoyan

doi:10.1109/iciba50161.2020.9276804

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Although human activity causes fluctuation of both amplitude and phase, many studies only use the amplitude information for ICC (like in [6], [11], [12]), mainly because the phase information often suffers from more severe hardware measurement noise such as carrier frequency offset and sampling time offset [21]- [23]. Instead of using raw phase measurements, Zong et al [13] computed the phase difference between adjacent antennas as the input to an SVM-based ICC classifier. Liu et al [24] utilized a CNN to extract the features of the CSI amplitude and phase information and use both features to detect human presence.…”

Section: A Learning-based Wifi Icc Methodsmentioning

confidence: 99%

“…For phase data processing, due to hardware impairment of WiFi chips, such as carrier frequency offset, and sampling time offset [24], the CSI phase data often change abruptly in adjacent time slots. Here, we first use the "unwrap" function to correct phase jump, and then compute the phase difference between two adjacent receiving antennas to eliminate random phase noise [13], and denote the phase data after processing as H phd . With a bit abuse of notation, we denote the data samples in the ith segment as denoted as…”

Section: The 1st Segmentmentioning

confidence: 99%

“…The existing WiFi-based ICC methods can be mainly divided into two categories, depending on the need of manual feature extraction [6]- [13]. One relies on explicit manual features engineered from raw data, such as mean value, variance, median, and range, and then uses threshold-based or learningbased classifiers like support vector machine (SVM) to identify the crowd number.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sample-Efficient Cross-Domain WiFi Indoor Crowd Counting via Few-shot Learning

Hou

Zheng

et al. 2022

2022 31st Wireless and Optical Communications Conference (WOCC)

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Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. Recent development of WiFi-based ICC technology relies on detecting the variation of wireless channel state information (CSI) caused by human motions and has gained increasing popularity due to its low hardware cost, reliability under all lighting conditions, and privacy preservation in sensing data processing. To attain high estimation accuracy, existing WiFi-based ICC methods often require a large amount of labeled CSI training data samples for each application domain, i.e., a particular WiFi transceiver or background deployment. This makes large-scale deployment of WiFi-based ICC technology across dissimilar domains extremely difficult and costly. In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of fewshot learning (FSL) paradigm consisting of two major stages: source domain meta training and target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain dataset to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the highdimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few shots of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68%, and on average 96.37% detection accuracy in a 0-8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.

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Section: A Learning-based Wifi Icc Methodsmentioning

confidence: 99%

Section: The 1st Segmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sample-Efficient Cross-Domain WiFi Indoor Crowd Counting via Few-shot Learning

Hou

Zheng

et al. 2022

2022 31st Wireless and Optical Communications Conference (WOCC)

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show abstract

“…The function calculates the percentage of nonzero elements (PEM) in the dilated CSI matrix as an input to DNN. In [24], authors proposed a crowd counting system based on the phase difference extraction and FE space model. The proposed system is able to achieve an accuracy of 97% for eight classes based on the SVM model.…”

Section: B Csi-based Crowd Counting Systemsmentioning

confidence: 99%

CrossCount: Efficient Device-Free Crowd Counting by Leveraging Transfer Learning

Khan

2023

IEEE Internet Things J.

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Recently, wireless sensing is gaining immense attention in the Internet of things (IoT) for crowd counting and occupancy detection. As wireless signals propagate, they tend to scatter and reflect in various directions depending on the number of people in the indoor environment. The combined effect of these variations on wireless signals is characterized by the channel state information (CSI), which can be further exploited to identify the presence of people. State-of-the-art CSI-based supervised crowd counting systems are vulnerable to temporal and environmental dynamics in practical scenarios as their performance degrades with fluctuations in the indoor environments due to multipath fading. Inspired by the breakthroughs of transfer learning and advancement in edge computing, we have leveraged in this work the concept of transfer learning to minimize this problem via exploiting the trained model from source environment for other indoor environments to perform device-free crowd counting (CrossCount) at the target rooms. Our results show that this technique can combat the dynamics of the environment and achieves 4.7% better accuracy with 40% reduction in training time as compared to conventional convolutional neural networks. In essence, our results imply the future possibility of harnessing crowdsourced CSI data collected at different indoor environments to boost the accuracy and efficiency of local crowd counting systems.

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DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning

Hou

Zheng

et al. 2023

IEEE Internet Things J.

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Device-Free Crowd Counting Based on the Phase Difference of Channel State Information

Cited by 4 publications

References 12 publications

Sample-Efficient Cross-Domain WiFi Indoor Crowd Counting via Few-shot Learning

Sample-Efficient Cross-Domain WiFi Indoor Crowd Counting via Few-shot Learning

CrossCount: Efficient Device-Free Crowd Counting by Leveraging Transfer Learning

DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-Shot Learning

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