Que-Fi: A Wi-Fi Deep-Learning-Based Queuing People Counting

Zhang, Hao; Zhou, Mingzhang; Sun, Haixin; Guolin, Zhao; Qi, Jie; Wang, Junfeng; Esmaiel, Hamada

doi:10.1109/jsyst.2020.2994062

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…Wang et al 20 employed smartphone WiFi signals to track human queues by extracting unique features embedded in the signal traces to infer the critical time points when a person reaches the head of the queue, thereby deriving user service times. Zhang et al 21 introduced Que‐Fi scheme, a queue number identification system based on Wi‐Fi CSI and a deep learning network. Jiang et al 22 propose Pa‐Count, a mathematical inference method that use a priori probability to calculate the number of real‐time passengers through CSI signals.…”

Section: Related Workmentioning

confidence: 99%

STRmt: A state transition based model for real‐time crowd counting in a metro system

Sun,

Zhao,

Zhang

et al. 2024

Concurrency and Computation

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SummaryReal‐time estimation of crowd counting in underground metro systems, constrained by limited space, is crucial for managing heightened pedestrian volumes and responding promptly to emergencies. To address this challenge, we propose a passenger state transition‐based model, called STRmt, designed for the seamless and continuous monitoring of real‐time crowd movement within service areas of stations and trains, leveraging auto fare collection systems (AFC) as a comprehensive sensor network. Our innovation lies in modeling the dynamic movement of passengers within a metro system over time as a state transition process aligned with the train schedule. To achieve this, we introduce a spatio‐temporal deep learning framework, denoted as STnet, designed to dynamically predict these state transitions. The performance of our method is rigorously assessed through extensive experiments conducted spanning 2 years in Shenzhen, China, utilizing AFC data, train schedule data, and weather data. The results demonstrate that the proposed method surpasses baseline methods, achieving an estimation precision of 0.92.

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Section: Related Workmentioning

confidence: 99%

STRmt: A state transition based model for real‐time crowd counting in a metro system

Sun,

Zhao,

Zhang

et al. 2024

Concurrency and Computation

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“…However, technology no longer remains within the walls of an organization (Choudhury et al , 2020). With the escalation of remote work (Choudhury, 2020; Choudhury et al , 2020), the increase in wearable technology (Kang and Jung, 2020; Ogbanufe and Gerhart, 2020) and the ubiquity of Wi-Fi technology (Zhang et al , 2020), employees can and will work from anywhere (Fuller et al , 2020; Choudhury, 2020; Choudhury et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

The role of perceived attributes of innovations in the adoption of smartwatches

Wolverton

Hollier²,

Cahyanto³

et al. 2022

JSIT

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Purpose The purpose of this study is to gain a better understanding of the adoption of smartwatches. Specifically, the robustness of the perceived characteristics of innovation (PCI) model in predicting such adoption is demonstrated. Previous smartwatch research has not used this same technology adoption model. This research demonstrates the value of examining the adoption of wearables and other new technologies (i.e. smartwatches) with the new approach of PCI while avoiding some of the limitations of previous studies. Design/methodology/approach A survey of 178 respondents was conducted, and the data was analyzed using structured equation modeling and partial least squares. The model described here extends the models used in extant smartwatch research by identifying additional factors. Findings The results show that three factors (compatibility, trialability and relative advantage) significantly impact behavioral intention to adopt the technology. Originality/value With the escalation of remote work, the increase in wearable technology and the widespread use of Wi-Fi technology, the way that employees adopt and use their technology must be reassessed. Therefore, a new approach was sought with an established theoretical base to evaluate the adoption of smartwatches under these evolving circumstances. Specifically, Moore and Benbasat’s characterization of the PCI was selected, which is rooted in Rogers’ diffusion of innovation theory.

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“…Consequently, the application requirements for crowd counting are also increasing. By utilizing effective crowd counting schemes, relevant departments or enterprises can obtain real-time information on the number of people in a specific area, thereby allocating public resources more reasonably, reducing resource waste, and improving service quality [2,3]. For example, by counting the number of people applying for different businesses, more staff can be allocated to the business departments with larger queues.…”

mentioning

confidence: 99%

“…CSI-based wireless sensing technology has been developed for over a decade, and numerous CSI-based crowd counting algorithms have emerged [2][3][4][9][10][11][12][13][14][15][16]. In 2014, Xi et al [9] proposed the Electronic Frog Eye system, the first to use CSI information for crowd counting.…”

mentioning

confidence: 99%

“…Ma et al [13] proposed a device-free crowd density estimation system called Wisual, which predicted crowd density with 98% accuracy and accurately displayed the spectrum of mobile people based on CSI. Zhang et al [2] proposed a queueing crowd counting system based on CSI and deep learning networks, called Quee-Fi system, which used a static model based on fully connected Tian et al J Wireless Com Network (2023) 2023:79 neural networks with convolutional long short-term memory for queueing crowd counting. Zhang et al [3] proposed a WiFi-based cross-environment crowd counting system with the ability to estimate walking directions and perform crowd counting over only one link, called WiCrowd.…”

mentioning

confidence: 99%

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Texture features-based lightweight passive multi-state crowd counting algorithm

Tian,

Li,

et al. 2023

J Wireless Com Network

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Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting.

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Que-Fi: A Wi-Fi Deep-Learning-Based Queuing People Counting

Cited by 13 publications

References 16 publications

STRmt: A state transition based model for real‐time crowd counting in a metro system

STRmt: A state transition based model for real‐time crowd counting in a metro system

The role of perceived attributes of innovations in the adoption of smartwatches

Texture features-based lightweight passive multi-state crowd counting algorithm

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