Privacy-Preserving Pose Estimation for Human-Robot Interaction
- URL: http://arxiv.org/abs/2011.07387v1
- Date: Sat, 14 Nov 2020 21:09:53 GMT
- Title: Privacy-Preserving Pose Estimation for Human-Robot Interaction
- Authors: Youya Xia, Yifan Tang, Yuhan Hu and Guy Hoffman
- Abstract summary: 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.
- Score: 8.905235622945254
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pose estimation is an important technique for nonverbal human-robot
interaction. That said, the presence of a camera in a person's space raises
privacy concerns and could lead to distrust of the robot. In this paper, we
propose a privacy-preserving camera-based pose estimation method. The proposed
system consists of a user-controlled translucent filter that covers the camera
and an image enhancement module designed to facilitate pose estimation from the
filtered (shadow) images, while never capturing clear images of the user. We
evaluate the system's performance on a new filtered image dataset, considering
the effects of distance from the camera, background clutter, and film
thickness. Based on our findings, we conclude that our system can protect
humans' privacy while detecting humans' pose information effectively.
Related papers
- Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset [52.22758311559]
We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and Spot.
The key-novelty is the focus on the robot's perspective, i.e., on the data captured by the robot's sensors.
The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users.
arXiv Detail & Related papers (2024-03-21T14:53:50Z) - Human-Imperceptible Identification with Learnable Lensless Imaging [12.571999330435801]
We propose a learnable lensless imaging framework that protects visual privacy while maintaining recognition accuracy.
To make captured images imperceptible to humans, we designed several loss functions based on total variation, invertibility, and the restricted isometry property.
arXiv Detail & Related papers (2023-02-04T22:58:46Z) - Selective manipulation of disentangled representations for privacy-aware
facial image processing [5.612561387428165]
We propose an edge-based filtering stage that removes privacy-sensitive attributes before the sensor data are transmitted to the cloud.
We use state-of-the-art image manipulation techniques that leverage disentangled representations to achieve privacy filtering.
arXiv Detail & Related papers (2022-08-26T12:47:18Z) - Privacy-Preserving Face Recognition with Learnable Privacy Budgets in
Frequency Domain [77.8858706250075]
This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain.
Our method performs very well with several classical face recognition test sets.
arXiv Detail & Related papers (2022-07-15T07:15:36Z) - PrivHAR: Recognizing Human Actions From Privacy-preserving Lens [58.23806385216332]
We propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline.
Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks.
arXiv Detail & Related papers (2022-06-08T13:43:29Z) - OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak.
We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks.
The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
arXiv Detail & Related papers (2022-05-24T11:29:37Z) - Deep Learning Approach Protecting Privacy in Camera-Based Critical
Applications [57.93313928219855]
We propose a deep learning approach towards protecting privacy in camera-based systems.
Our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.
arXiv Detail & Related papers (2021-10-04T19:16:27Z) - SocialGuard: An Adversarial Example Based Privacy-Preserving Technique
for Social Images [6.321399006735314]
We propose a novel adversarial example based privacy-preserving technique for social images against object detectors based privacy stealing.
We use two metrics, privacy-preserving success rate and privacy leakage rate, to evaluate the effectiveness of the proposed method.
The privacy-preserving success rates of the proposed method on MS-COCO and PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively.
arXiv Detail & Related papers (2020-11-27T05:12:47Z) - 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.