About: Monitoring physical distancing for crowd management: real-time trajectory and group analysis

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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	Monitoring physical distancing for crowd management: real-time trajectory and group analysis
Creator	Corbetta, Alessandro Pouw, Caspar Toschi, Federico Van Schadewijk, Frank
Source	ArXiv
abstract	Physical distancing, as a measure to contain the spreading of Covid-19, is defining a%22new normal%22. Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are:%22in which conditions non-family members infringed social distancing?%22,%22Are there repeated offenders?%22, and%22How are new crowd management measures performing?%22. Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we have a two-fold aim: first, we propose an efficient and scalable analysis framework to process, offline or in real-time, pedestrian tracking data via a sparse graph. The framework tackles efficiently all the questions mentioned above, representing pedestrian-pedestrian interactions via vector-weighted graph connections. On this basis, we can disentangle distance offenders and family members in a privacy-compliant way. Second, we present a thorough analysis of mutual distances and exposure-times in a Dutch train platform, comparing pre-Covid and current data via physics observables as Radial Distribution Functions. The versatility and simplicity of this approach, developed to analyze crowd management measures in public transport facilities, enable to tackle issues beyond physical distancing, for instance the privacy-respectful detection of groups and the analysis of their motion patterns.
has issue date	2020-07-14(xsd:dateTime)
has license	arxiv
sha1sum (hex)	47c92a34adc0de45d81a5400d01570569bea1fd1
resource representing a document's title	Monitoring physical distancing for crowd management: real-time trajectory and group analysis
resource representing a document's body	covid:47c92a34adc0de45d81a5400d01570569bea1fd1#body_text
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