Low cost crowd counting using audio tones

Kannan, Pravein Govindan; Venkatagiri, Seshadri Padmanabha; Chan, Mun Choon; Ananda, A.L.; Peh, Li-Shiuan

doi:10.1145/2426656.2426673

Cited by 80 publications

(36 citation statements)

References 32 publications

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“…networks with frequent topology changes due to fluctuations in link quality and node movement. Within this scope, the state-of-the-art achieves an accuracy between 3% and 35% for 25 smartphones, relying on audio signals [4]. Other studies, using bluetooth signals in smartphones [3] and radio signals in sensor nodes [5], achieve comparable results for similar settings.…”

Section: Introductionmentioning

confidence: 87%

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Lightweight neighborhood cardinality estimation in dynamic wireless networks

Cattani

Zúñiga

Loukas

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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Abstract-We address the problem of estimating the neighborhood cardinality of nodes in dynamic wireless networks. Different from previous studies, we consider networks with high densities (a hundred neighbors per node) and where all nodes estimate cardinality concurrently. Performing concurrent estimations on dense mobile networks is hard; we need estimators that are not only accurate, but also fast, asynchronous (due to mobility) and lightweight (due to concurrency and high density). To cope with these requirements, we propose Estreme, a neighborhood cardinality estimator with extremely low overhead that leverages the rendezvous time of low-power medium access control (MAC) protocols. We implemented Estreme on the Contiki OS and show a significant improvement over the state-of-the-art. With Estreme, 100 nodes can concurrently estimate their neighborhood cardinality with an error of ≈10%. State-of-the-art solutions provide a similar accuracy, but on networks consisting of a few tens of nodes and where only a fraction of nodes estimate the cardinality concurrently.

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Section: Introductionmentioning

confidence: 87%

“…Initial studies have used mobile phones to estimate the density of crowds. The most relevant work uses audio tones to count neighbor devices [4]. The main challenge involved is to successfully transmit data packets using lowquality speakers/mic-rophones, as well as to cope with the presence of environmental auditive noise.…”

Section: Related Workmentioning

confidence: 99%

Lightweight neighborhood cardinality estimation in dynamic wireless networks

Cattani

Zúñiga

Loukas

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Jens et al [6] exploited Bluetooth collecting environment data to estimate crowd density. Kannan et al [7] utilized the headset to estimate the crowd density(the number of phones exactly). Phone inertial sensors [8] can also be used for localization.…”

Section: A Device-based Approachmentioning

confidence: 99%

“…These device-based approaches [2], [3], [4], [5], [6], [7], [8] require people to carry certain devices for surveillance, which significantly constrains the usage scope. For a public area with mass people, distributing the device to each person is impractical and costly, and may not be doable under emergent events.…”

Section: Introductionmentioning

confidence: 99%

Electronic frog eye: Counting crowd using WiFi

Zhao

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

363

169

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Abstract-Crowd counting, which count or accurately estimate the number of human beings within a region, is critical in many applications, such as guided tour and crowd control. A crowd counting solution should be scalable and be minimally intrusive (i.e., device-free) to users. Image-based solutions are device-free, but cannot work well in a dim or dark environment. Non-image based solutions usually require every human being carrying device, and are inaccurate and unreliable in practice. In this paper, we present FCC, a device-Free Crowd Counting approach based on Channel State Information (CSI). Our design is motivated by our observation that CSI is highly sensitive to environment variation, like a frog eye. We theoretically discuss the relationship between the number of moving people and the variation of wireless channel state. A major challenge in our design of FCC is to find a stable monotonic function to characterize the relationship between the crowd number and various features of CSI. To this end, we propose a metric, the Percentage of nonzero Elements (PEM), in the dilated CSI Matrix. The monotonic relationship can be explicitly formulated by the Grey Verhulst Model, which is used for crowd counting without a labor-intensive site survey. We implement FCC using off-theshelf IEEE 802.11n devices and evaluate its performance via extensive experiments in typical real-world scenarios. Our results demonstrate that FCC outperforms the state-of-art approaches with much better accuracy, scalability and reliability.

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“…Multiple tones can be detected by running the algorithm multiple times, unlike the FFT which calculates for all bins simultaneously. Examples include structural health monitoring [2], instrument tuning [3], power metering [4] and active damping [5], voice [6] and underwater communications [7], security applications such as fingerprint identification [10], and mobile applications such as crowd counting [8] and gesture sensing [9].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Goertzel Algorithm for Low-Power, Embedded Systems

Brown¹,

Timoney²,

Lysaght³

2014

25th IET Irish Signals &Amp; Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communi

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-The Goertzel Algorithm provides an efficient mechanism for tone detection in signal processing. However, on Wireless Sensor Network nodes, there is often not sufficient processing power to implement this using floating-point arithmetic, so fixed-point implementations are used. In this paper we compare the quality of the results for a typical audio application with different resolutions of fixed-point support using MATLAB. We also provide performance figures based on implementing this on a representative sensor node platform. Our results show that, for this application-space, 16-bit fixed-point arithmetic provides the best tradeoff of accuracy against performance. No previous work has addressed this specific aspect of implementating the Goertzel algorithm.

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Low cost crowd counting using audio tones

Cited by 80 publications

References 32 publications

Lightweight neighborhood cardinality estimation in dynamic wireless networks

Lightweight neighborhood cardinality estimation in dynamic wireless networks

Electronic frog eye: Counting crowd using WiFi

An Evaluation of the Goertzel Algorithm for Low-Power, Embedded Systems

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