How low can you go? Privacy-preserving people detection with an omni-directional camera

• URL: http://arxiv.org/abs/2007.04678v1
• Date: Thu, 9 Jul 2020 10:10:23 GMT
• Title: How low can you go? Privacy-preserving people detection with an omni-directional camera
• Authors: Timothy Callemein, Kristof Van Beeck, and Toon Goedem\'e
• Abstract summary: In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available.
• Score: 2.433293618209319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available. If these devices can be integrated in an embedded low-power sensor, it would form an ideal extension of automated room reservation systems in office environments. The main challenge we target here is ensuring the privacy of the people filmed. The approach we propose is going to extremely low image resolutions, such that it is impossible to recognise people or read potentially confidential documents. Therefore, we retrained a single-shot low-resolution person detection network with automatically generated ground truth. In this paper, we prove the functionality of this approach and explore how low we can go in resolution, to determine the optimal trade-off between recognition accuracy and privacy preservation. Because of the low resolution, the result is a lightweight network that can potentially be deployed on embedded hardware. Such embedded implementation enables the development of a decentralised smart camera which only outputs the required meta-data (i.e. the number of persons in the meeting room).

Related papers

• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras [9.801893730708134]
  In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. For accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigate cost-effective methods that are suitable for person detection based on low-resolution infrared images
  arXiv  Detail & Related papers  (2022-09-22T22:20:30Z)
• Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities [66.4525391417921]
  We design and evaluate a multi-sensor drone detection system. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
  arXiv  Detail & Related papers  (2022-07-05T10:00:58Z)
• Lasers to Events: Automatic Extrinsic Calibration of Lidars and Event Cameras [67.84498757689776]
  This paper presents the first direct calibration method between event cameras and lidars. It removes dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements.
  arXiv  Detail & Related papers  (2022-07-03T11:05:45Z)
• Monitoring social distancing with single image depth estimation [39.79652626235862]
  Single image depth estimation can be a viable alternative to other depth perception techniques. Our framework can run reasonably fast and comparably to competitors, even on pure CPU systems.
  arXiv  Detail & Related papers  (2022-04-04T17:58:02Z)
• Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking [114.16306938870055]
  We propose a coordinate-aligned multi-camera collaboration system for AMOT. In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution. Our system achieves a coverage of 71.88%, outperforming the baseline method by 8.9%.
  arXiv  Detail & Related papers  (2022-02-22T13:28:40Z)
• In-Bed Person Monitoring Using Thermal Infrared Sensors [53.561797148529664]
  We use 'Griddy', a prototype with a Panasonic Grid-EYE, a low-resolution infrared thermopile array sensor, which offers more privacy. For this purpose, two datasets were captured, one (480 images) under constant conditions, and a second one (200 images) under different variations. We test three machine learning algorithms: Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Neural Network (NN)
  arXiv  Detail & Related papers  (2021-07-16T15:59:07Z)
• Privacy-Preserving Pose Estimation for Human-Robot Interaction [8.905235622945254]
  We propose a privacy-preserving camera-based pose estimation method. The proposed system consists of a user-controlled translucent filter that covers the camera. We evaluate the system's performance on a new filtered image dataset.
  arXiv  Detail & Related papers  (2020-11-14T21:09:53Z)
• Perceiving Humans: from Monocular 3D Localization to Social Distancing [93.03056743850141]
  We present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule.
  arXiv  Detail & Related papers  (2020-09-01T10:12:30Z)
• Anyone here? Smart embedded low-resolution omnidirectional video sensor to measure room occupancy [2.7347781746626705]
  We present a room occupancy sensing solution with unique properties. It is based on an omnidirectional vision camera, rich capturing scene info over a wide angle. No privacy issues arise because its extremely low image resolution, rendering people unrecognisable.
  arXiv  Detail & Related papers  (2020-07-09T17:05:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.