Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras

• URL: http://arxiv.org/abs/2209.11335v1
• Date: Thu, 22 Sep 2022 22:20:30 GMT
• Title: Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras
• Authors: Thomas Dubail, Fidel Alejandro Guerrero Pe\~na, Heitor Rapela Medeiros, Masih Aminbeidokhti, Eric Granger, Marco Pedersoli
• Abstract summary: 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
• Score: 9.801893730708134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 compact embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. However, 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. Results indicate that for such images, we can reduce the amount of supervision and computation, while still achieving a high level of detection accuracy. Going from single-shot detectors that require bounding box annotations of each person in an image, to auto-encoders that only rely on unlabelled images that do not contain people, allows for considerable savings in terms of annotation costs, and for models with lower computational costs. We validate these experimental findings on two challenging top-view datasets with low-resolution infrared images.

Related papers

• Spectral Sensitivity Estimation Without a Camera [6.599344783327053]
  A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. We propose a framework for spectral sensitivity estimation that does not require any hardware, but also does not require physical access to the camera itself. We provide our code and predicted sensitivities for 1,000+ cameras, and discuss which tasks can become trivial when camera responses are available.
  arXiv  Detail & Related papers  (2023-04-23T06:18:07Z)
• DensePose From WiFi [86.61881052177228]
  We develop a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. Our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches.
  arXiv  Detail & Related papers  (2022-12-31T16:48:43Z)
• Person Detection Using an Ultra Low-resolution Thermal Imager on a Low-cost MCU [5.684789481921423]
  We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32x24 pixel static imager. Our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers.
  arXiv  Detail & Related papers  (2022-12-16T11:27:50Z)
• 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)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• Towards Online Monitoring and Data-driven Control: A Study of Segmentation Algorithms for Laser Powder Bed Fusion Processes [83.97264034062673]
  An increasing number of laser powder bed fusion machines use off-axis infrared cameras to improve online monitoring and data-driven control capabilities. We study over 30 segmentation algorithms that segment each infrared image into a foreground and background. The identified algorithms can be readily applied to the laser powder bed fusion machines to address each of the above limitations and thus, significantly improve process control.
  arXiv  Detail & Related papers  (2020-11-18T03:30:16Z)
• Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras [12.411387535687556]
  We consider this challenge of occupancy detection using extremely low-quality, privacy-preserving images from low power image sensors. We propose a combined few shot learning and clustering algorithm to address this challenge that has very low commissioning and maintenance cost.
  arXiv  Detail & Related papers  (2020-08-13T02:47:01Z)
• Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments [35.342730238802886]
  We present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras. In each spectrum, a customized RetinaNet network with ResNet backbone provides human detections which are subsequently fused to minimize the overall false detection rate. We show that by optimizing the bounding box anchors and augmenting the image resolution the number of missed detections from high altitudes can be decreased by over 20 percent.
  arXiv  Detail & Related papers  (2020-08-10T15:30:42Z)
• Learning Camera Miscalibration Detection [83.38916296044394]
  This paper focuses on a data-driven approach to learn the detection of miscalibration in vision sensors, specifically RGB cameras. Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric. By training a deep convolutional neural network, we demonstrate the effectiveness of our pipeline to identify whether a recalibration of the camera's intrinsic parameters is required or not.
  arXiv  Detail & Related papers  (2020-05-24T10:32:49Z)

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.