A Statistical Approach in Designing an RF-Based Human Crowd Density Estimation System

Fadhlullah, Solahuddin Yusuf; Ismail, Widad

doi:10.1155/2016/8351017

Cited by 23 publications

(16 citation statements)

References 26 publications

(33 reference statements)

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“…It is evident that when the number of people is low (e.g. 1-2), the curve is less steep, while when it is high (e.g., [4][5][6][7][8][9][10][11][12][13][14][15][16], the curve is steeper. Therefore, we propose to use as features metrics characterizing the trend of this curve.…”

Section: Feature Extraction From Csi Vector Differences and Svdmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Wei Xu . more and more interest, is based on the use of opportunistic RF signals [2]- [6]. The idea is to process the received RF signals of wireless networks to extract the changes of the propagation channel induced by the presence of different number of people.…”

Section: Introductionmentioning

confidence: 99%

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LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

et al. 2019

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The rising need of crowd monitoring in public spaces, especially for safety purposes, pushes the research community to propose and experiment novel methods of crowd density estimation. This paper focuses on device-free RF sensing, which does not require the monitored people to carry any electronic device. In particular, the paper proposes and assesses the performance of a crowd density estimation system based on the analysis of the variations of Channel State Information (CSI) computed from unencrypted synchronization signals transmitted by a eNodeB and reflected/scattered by people located in the monitored area. The proposed method uses features extracted from the list of singular values of the CSI secant set. This approach allows to reduce the impact of CSI variations due to noise or HW instability, improving the sensitivity to CSI variations caused by human presence. The average accuracy achieved by the proposed approach is 84%, which is comparable with the accuracy achieved with WiFi based crowd density estimation systems.

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Section: Feature Extraction From Csi Vector Differences and Svdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

et al. 2019

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“…A number of approaches [43][44][45] estimate crowd density based on variations of the received signal strength indicator (RSSI) values of a Wi-Fi network. Under controlled experiments they observe that the more people (obstacles) are present, the greater the RSSI variations.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

“…Under controlled experiments they observe that the more people (obstacles) are present, the greater the RSSI variations. Fadhlullah and Ismail [43] apply analysis of variance, Yuan et al [44] apply k-means clustering to obtain clusters of similar crowd density based on similar RSSI variations, while Yoshida et al [45] apply linear and support vector machines regression. Our work also deals with the fact that greater crowd leads to greater RSSI variation (represented by the width of the rings in Fig.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

et al. 2019

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Crowd disasters have taken many human lives. The Love Parade disaster in Duisburg, 2010, the Ellis Park Stadium disaster in Johannesburg, 2001, the PhilSports Stadium stampede in Manila, 2006, are just a few examples. One of the major factors contributing to crowd disasters are critically dense spots [1-3], which are difficult to detect due to lack of macroscopic overview of the crowd [1]. In this paper we address the problem of estimating the crowd density distribution in situations such as indoor dance events, to enable prevention of crowd disasters. A lot of research on estimating crowd density concerns processing video records from security cameras [4, 5]. However, this approach does not suffice to detect critically raised crowd density. Firstly, as mentioned before, it is difficult to obtain macroscopic overview of the crowd. Secondly, the lighting conditions at a concert might not be sufficient for video-based crowd analysis. Finally, the error of counting people increases with the increase of the actual crowd density [6] due to the so-called occlusion effects. Another way to monitor the crowd density is by using RFID technology [3]. Each participant is asked to wear a tag, and RFID readers are distributed across the venue. This approach, however, requires participation from the crowd and deployment costs.

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“…A number of approaches [43,44,45] The authors then use statistics to combine the different measurements in space and time.…”

Section: Comparison To Previous Workmentioning

confidence: 99%

Detecting high indoor crowd density with Wi-Fi localization: A statistical mechanics approach

Georgievska¹,

Rutten²,

Amoraal³

et al. 2019

Preprint

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We address the problem of detecting highly raised crowd density in situations such as indoor dance events.We propose a new method for estimating crowd density by anonymous, non-participatory, indoor Wi-Fi localization of smart phones. Using a probabilistic model inspired by statistical mechanics, and relying only on big data analytics, we tackle three challenges: (1) the ambiguity of Wi-Fi based indoor positioning, which appears regardless of whether the latter is performed with machine learning or with optimization, (2) the MAC address randomization when a device is not connected, and (3) the volatility of packet interarrival times. The main result is that our estimation becomes more -- rather than less -- accurate when the crowd size increases. This property is crucial for detection of dangerous crowd density.

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A Statistical Approach in Designing an RF-Based Human Crowd Density Estimation System

Cited by 23 publications

References 26 publications

LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

LTE Signals for Device-Free Crowd Density Estimation Through CSI Secant Set and SVD

Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

Detecting high indoor crowd density with Wi-Fi localization: A statistical mechanics approach

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