Simultaneous Crowd Counting and Localization by WiFi CSI

Choi, Hyuckjin; Matsui, Tomokazu; Fujimoto, Manato; Yasumoto, Keiichi

doi:10.1145/3427796.3430000

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Guo et al [38] consider using CSI for estimating human dynamics, including participant number estimation, human density estimation, walking speed, and direction derivation. Choi et al [39] consider using CSI for crowd counting and localization. CSI-based approaches mostly operate in different indoor environments with limited numbers of people [40], and they try to model the channel state by configuration in indoor setups.…”

Section: B Sensing and Wireless Approachesmentioning

confidence: 99%

CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

Solmaz¹,

Baranwal

Cirillo³

2022

2022 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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The widespread use of pervasive sensing technologies such as wireless sensors and street cameras allows the deployment of crowd estimation solutions in smart cities. However, existing Wi-Fi-based systems do not provide highly accurate crowd size estimation. Furthermore, these systems do not adapt to the dynamic changes in-the-wild, such as unexpected crowd gatherings. This paper presents a new adaptive machine learning system, called CountMeIn, to address the crowd estimation problem using polynomial regression and neural networks. The approach transfers the calibration task from cameras to machine learning after a short training with people counting from stereoscopic cameras, Wi-Fi probe packets, and temporal features. After the training, CountMeIn calibrates Wi-Fi using the trained model and maintains high accuracy for a longer duration without cameras. We test the approach in our pilot study in Gold Coast, Australia, for about five months. CountMeIn achieves 44% and 72% error reductions in minutely and hourly crowd estimations compared to the state-of-the-art methods.

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Section: B Sensing and Wireless Approachesmentioning

confidence: 99%

CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

Solmaz¹,

Baranwal

Cirillo³

2022

2022 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Leveraging ambient sensing for the estimation of curiosity-driven human crowd

Das

Narayan

Chakraborty

2022

2022 IEEE International Systems Conference (SysCon)

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ECC: Passenger Counting in the Elevator Using Commodity WiFi

Chen

et al. 2022

Applied Sciences

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Elevators have become a kind of indispensable facility for everyday life, which bring people both convenience and safety hazards. Specifically in the household environment, an elevator’s lifespan is expected to be more than 20 years. An appropriate and regularly maintained counterweight is conducive to extending elevator life. This paper proposes a passenger counting approach in the elevator for regular counterweight adjustment based on commodity WiFi called ECC. Since the running time of the elevator between two adjacent floors is short, the major challenge of ECC is how to count passengers from the limited captured data. This paper first theoretically analyzes the relationship between the number of passengers and the variation of channel state information (CSI). Then ECC constructs a multi-dimensional feature by extracting the average of amplitude (AOA), time-varying spectrum (TVS), and percentage of non-zero elements (PEM) features from the limited data. Finally, the random forest (RF) classifier is used for passenger counting and the local optimization problem is solved by expanding the feature dataset through data segmentation. ECC is implemented by using off-the-shelf IEEE 802.11n devices, and its performance is evaluated via extensive experiments in typical real-world scenes. The estimated precision of ECC can reach more than 95%, and more than 97% of estimation errors are less than 2 persons, which demonstrates the superior effectiveness and generalizability of ECC.

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Simultaneous Crowd Counting and Localization by WiFi CSI

Cited by 3 publications

References 4 publications

CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

Leveraging ambient sensing for the estimation of curiosity-driven human crowd

ECC: Passenger Counting in the Elevator Using Commodity WiFi

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