Anyone here? Smart embedded low-resolution omnidirectional video sensor to measure room occupancy

• URL: http://arxiv.org/abs/2007.04934v1
• Date: Thu, 9 Jul 2020 17:05:32 GMT
• Title: Anyone here? Smart embedded low-resolution omnidirectional video sensor to measure room occupancy
• Authors: Timothy Callemein, Kristof Van Beeck and Toon Goedem\'e
• Abstract summary: 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.
• Score: 2.7347781746626705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a room occupancy sensing solution with unique properties: (i) It is based on an omnidirectional vision camera, capturing rich scene info over a wide angle, enabling to count the number of people in a room and even their position. (ii) Although it uses a camera-input, no privacy issues arise because its extremely low image resolution, rendering people unrecognisable. (iii) The neural network inference is running entirely on a low-cost processing platform embedded in the sensor, reducing the privacy risk even further. (iv) Limited manual data annotation is needed, because of the self-training scheme we propose. Such a smart room occupancy rate sensor can be used in e.g. meeting rooms and flex-desks. Indeed, by encouraging flex-desking, the required office space can be reduced significantly. In some cases, however, a flex-desk that has been reserved remains unoccupied without an update in the reservation system. A similar problem occurs with meeting rooms, which are often under-occupied. By optimising the occupancy rate a huge reduction in costs can be achieved. Therefore, in this paper, we develop such system which determines the number of people present in office flex-desks and meeting rooms. Using an omnidirectional camera mounted in the ceiling, combined with a person detector, the company can intelligently update the reservation system based on the measured occupancy. Next to the optimisation and embedded implementation of such a self-training omnidirectional people detection algorithm, in this work we propose a novel approach that combines spatial and temporal image data, improving performance of our system on extreme low-resolution images.

Related papers

• CamLoPA: A Hidden Wireless Camera Localization Framework via Signal Propagation Path Analysis [59.86280992504629]
  CamLoPA is a training-free wireless camera detection and localization framework. It operates with minimal activity space constraints using low-cost commercial-off-the-shelf (COTS) devices. It achieves 95.37% snooping camera detection accuracy and an average localization error of 17.23, under the significantly reduced activity space requirements.
  arXiv  Detail & Related papers  (2024-09-23T16:23:50Z)
• VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
  We introduce a novel methodology that extends Pose Graph Optimization techniques. We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step. Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
  arXiv  Detail & Related papers  (2024-03-25T17:47:03Z)
• A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing [1.7092183947364459]
  We present a camera-based indoor localization and multi-person tracking system implemented on edge computing devices within a large indoor space. Our pipeline demonstrated an average localization error of 1.41 meters, a multiple-object tracking accuracy score of 88.6%, and a mean absolute body orientation error of 29degree.
  arXiv  Detail & Related papers  (2023-05-08T21:38:42Z)
• EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks [107.62975594230687]
  We demonstrate an application where event cameras excel: accurately estimating the impact location of fast-moving objects. We introduce a lightweight event representation called Binary Event History Image (BEHI) to encode event data at low latency. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms.
  arXiv  Detail & Related papers  (2023-04-14T15:23:28Z)
• 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)
• 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)
• 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)
• Unveiling personnel movement in a larger indoor area with a non-overlapping multi-camera system [23.195588088063577]
  The paper expands the scope of indoor movement perception based on non-overlapping multiple cameras. It improves the accuracy of pedestrian re-identification without introducing additional types of sensors.
  arXiv  Detail & Related papers  (2021-04-10T01:44:26Z)
• Estimating indoor occupancy through low-cost BLE devices [2.462983746099006]
  This article presents a low-cost system for occupancy detection. It builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. On average, in different environments, we can correctly classify the occupancy with an accuracy of 97.97%.
  arXiv  Detail & Related papers  (2021-01-30T09:54:31Z)
• 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)
• How low can you go? Privacy-preserving people detection with an omni-directional camera [2.433293618209319]
  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.
  arXiv  Detail & Related papers  (2020-07-09T10:10:23Z)

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.