About: Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects
Creator	Lee, Michael Zhang, Pan Bibliowicz, Jacky Calin, Liviu Lee, Bokyung Tessier, Alex Torres Calderon, Wilfredo
Source	ArXiv
abstract	The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads. To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem. Computer vision 3D reconstruction approaches project objects and the human body from the 2D image domain to 3D and perform 3D space intersection directly. However, this solution would not meet the accuracy requirement in applications due to projection error. Another standard approach is to train a neural network to infer touch actions from the collected visual data. This strategy would require significant amounts of training data to generalize over scale and viewpoint variations. A different approach to this problem is to identify whether a person has touched a defined object. In this work, we show that the solution to this problem can be straightforward. Specifically, we show that the contact between an object and a static surface can be identified by projecting the object onto the static surface through two different viewpoints and analyzing their 2D intersection. The object contacts the surface when the projected points are close to each other; we call this cross view projection consistency. Instead of doing 3D scene reconstruction or transfer learning from deep networks, a mapping from the surface in the two camera views to the surface space is the only requirement. For planar space, this mapping is the Homography transformation. This simple method can be easily adapted to real-life applications. In this paper, we apply our method to do office occupancy detection for studying the COVID-19 transmission pattern from an office desk in a meeting room using the contact information.
has issue date	2020-08-18(xsd:dateTime)
has license	arxiv
sha1sum (hex)	029409bcd00b3649b74a5a4cd3078517cedbdbaf
resource representing a document's title	Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects
resource representing a document's body	covid:029409bcd00b3649b74a5a4cd3078517cedbdbaf#body_text
is schema:about of	named entity 'space' named entity 'intersection' named entity 'contact' named entity 'simple' named entity 'generalize' named entity 'contacts' named entity 'SURFACES' named entity 'DETERMINE' named entity 'PROJECT' named entity 'DIRECTLY' named entity 'USEFUL' named entity 'CAMERA' named entity 'REAL' named entity 'METHOD' named entity 'TRANSMISSION PATTERN' named entity 'CONTACT INFORMATION' named entity 'COMPUTER VISION' named entity 'CONSISTENCY' named entity 'UNDERSTANDING' named entity 'ADAPTED' named entity 'OUR' named entity 'PAPER' named entity 'CONTACT' named entity 'DUE TO' named entity 'LIFE' named entity 'TRAIN' named entity 'approach' named entity 'hard' named entity 'consistency' named entity 'close' named entity 'problem' named entity 'data' named entity 'training data' named entity 'surface' named entity 'determine' named entity 'Instead' named entity 'occupancy' named entity 'variations' named entity 'viewpoints' named entity 'transfer learning' named entity 'static' named entity 'domain' named entity 'data' named entity 'studying' named entity '2D image' named entity 'neural network' named entity '3D reconstruction' named entity 'computing' named entity 'case study' named entity 'probability' named entity 'data set' named entity 'privacy protection' named entity 'neural network' named entity 'Homography' named entity 'COVID-19' named entity 'Manhattan distance' named entity 'algorithm' named entity 'commodity hardware' named entity '4.3' named entity 'machine learning algorithm' named entity 'Action recognition' named entity 'regression task' named entity 'histogram' named entity 'stereo camera' named entity 'COVID-19' named entity 'radiometry' named entity '3D reconstruction' named entity 'COVID-19' named entity 'computing' named entity 'action recognition' named entity 'machine learning' named entity 'time series' named entity 'computing' named entity 'grocery store' named entity 'Convolution' named entity 'formal proof' named entity '3D pose estimation' named entity 'algorithm' named entity 'computing' named entity 'structured light' named entity 'CAD model'

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