Privacy-preserving People Detection Enabled by Solid State LiDAR

Günter, Andrei; Boker, Stephan; König, Matthias; Hoffmann, Martin

doi:10.1109/ie49459.2020.9154970

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Their method combines partial body detection and person re‐identification to accurately count individuals in overlapping areas. In contrast, recent works, such as References 7 and 8, have employed LiDAR sensors, which, compared to video camera techniques, address privacy issues. However, in both solutions, hardware costs play a pivotal role and the suitability of LiDAR in different environmental scenarios remain problematic.…”

Section: Related Workmentioning

confidence: 99%

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Privacy‐preserving WiFi‐based crowd monitoring

Rusca,

Carluccio,

Casetti

et al. 2024

Trans Emerging Tel Tech

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The process of estimating the number of individuals within a defined area, commonly referred to as people counting, is of paramount importance in the realm of safety, security and crisis management. It serves as a crucial tool for accurately monitoring crowd dynamics and facilitating well‐informed decision‐making during critical situations. In our current study, we place a special emphasis on the utilization of the WiFi fingerprint technique, leveraging probe request messages emitted by smart devices as a proxy for people counting. However, it is essential to recognize the evolving landscape of privacy regulations and the concerted efforts by major smart‐device manufacturers to enhance user privacy, exemplified by the introduction of MAC addresses randomization techniques. In this context, we designed a crowd monitoring solution that exploits Bloom filters for ensuring a formal deniability, aligning with the stringent requirements set forth by regulations like the European GDPR. Our proposed solution not only addresses the essential task of people counting but also incorporates advanced privacy‐preserving mechanisms. Importantly, it seamlessly integrates with trajectory‐based crowd monitoring, offering a comprehensive approach to managing crowds while respecting individual privacy rights.

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Section: Related Workmentioning

confidence: 99%

“…Figure 5 shows the relative error in estimating the common MAC addresses, based on the complete data set from which the results in Table II were derived. The relative error is evaluated through (7).…”

Section: A Numerical Evaluationmentioning

confidence: 99%

Privacy‐preserving WiFi‐based crowd monitoring

Rusca,

Carluccio,

Casetti

et al. 2024

Trans Emerging Tel Tech

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“…First of all, the high hardware cost due to the high resource-computation demand required, secondly, outdoor scenarios are very challenging due to light variations and in presence of large density of people; finally, recording and storing face detection is subject to privacy issues. Furthermore, recent works [6], [7] exploited the use of LiDAR sensors, which compared to video camera techniques solve the privacy issues, but still the hardware cost and the environment use case remain a problem. Conversely, the works in [8] and [9] analyze the use of WiFi probe request messages as a method to monitor crowds in various scenarios by collecting WiFi fingerprints of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Aware Crowd Monitoring and WiFi Traffic Emulation for Effective Crisis Management

Rusca,

Carluccio,

Gasco

et al. 2023

2023 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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Estimating the number of people in a given area, denoted as "people counting" process, plays a vital role in crisis management and disaster response, enabling accurate monitoring of crowd dynamics and facilitating effective decision-making. In this work, we focus on WiFi fingerprint technique which exploits the MAC address of the mobile devices as proxy for people counting. Due to the European GDPR regulation and the strict actions undertaken by the major smart-devices vendors to enhance users' privacy (e.g., MAC randomization), most of the techniques investigated in the past must be redesigned and rethought. Here, we propose an ad-hoc WiFi traffic generator, tailored to emulate a realistic behaviour of the WiFi cards and to provide the ground truth for the counting algorithms. Furthermore, we propose a technique for crowd monitoring that leverages Bloom filters to guarantee a formal "deniability" property, which preserves users' privacy. Our solution is also compatible with trajectory-based crowd monitoring.

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“…Recently, light detection and ranging (LiDAR) sensors have received a great deal of attention in various fields, such as navigation systems for robots, indoor mapping on mobile devices, and in-home patient monitoring. Because LiDAR sensors generate images with depth information but without detailed red, green, and blue (RGB) information that differentiates individuals, it is considered more suitable for privacy-sensitive applications [ 8 , 9 ]. Figure 1 shows examples of 2D RGB images [ 10 ] and the 3D depth images generated from the 2D RGB images by a neural network [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An Indoor-Monitoring LiDAR Sensor for Patients with Alzheimer Disease Residing in Long-Term Care Facilities

Joo

Kim

et al. 2022

Sensors

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This paper introduces an indoor-monitoring LiDAR sensor for patients with Alzheimer disease residing in long-term care facilities (LTCFs), and this sensor exploits an optoelectronic analog front-end (AFE) to detect light signals from targets by utilizing on-chip avalanche photodiodes (APDs) realized in a 180 nm CMOS process and a neural processing unit (NPU) used for motion detection and decisions, especially for incidents of falls occurring in LTCFs. The AFE consists of an on-chip CMOS P+/N-well APD, a linear-mode transimpedance amplifier, a post-amplifier, and a time-to-digital converter, whereas the NPU exploits network sparsity and approximate processing elements for low-power operation. This work provides a potential solution of low-cost, low-power, indoor-monitoring LiDAR sensors for patients with Alzheimer disease in LTCFs.

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Privacy-preserving People Detection Enabled by Solid State LiDAR

Cited by 16 publications

References 13 publications

Privacy‐preserving WiFi‐based crowd monitoring

Privacy‐preserving WiFi‐based crowd monitoring

Privacy-Aware Crowd Monitoring and WiFi Traffic Emulation for Effective Crisis Management

An Indoor-Monitoring LiDAR Sensor for Patients with Alzheimer Disease Residing in Long-Term Care Facilities

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