Evidence map›Paper›PMID 35291397›Full record

ArticleMaterials today. Proceedings2022

Social distance monitoring system using deep learning and entry control system for commercial application.

T V Vishnu Kumar, Andrew John, M Vighnesh, M Jagannath

Abstract read
In one paragraph

Article in Materials today. Proceedings, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

T V Vishnu KumarSchool of Electronics Engineering, Vellore Institute of Technology Chennai, Tamil Nadu, India.
Andrew JohnSchool of Electronics Engineering, Vellore Institute of Technology Chennai, Tamil Nadu, India.
M VighneshSchool of Electronics Engineering, Vellore Institute of Technology Chennai, Tamil Nadu, India.
M JagannathSchool of Electronics Engineering, Vellore Institute of Technology Chennai, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For the last few months, the world has been under an astringent lockdown due to COVID-19. The number of COVID-19 cases is incrementing steadily. Even though scientists have found a vaccine for the obviation of the virus, the threat of being affected is high when we head out. Thus, one of the most efficacious modes of aversion is social distancing and home quarantine. As this was a sudden outbreak, people have not stocked up supplies and most of their personal work has been halted. Therefore, when people start to go outside, with or without a vaccine, it will be arduous to follow social distancing in countries, which are densely populated. With this in mind, this paper proposes a system that can be used in commercial spaces such as shops, banks, malls, offices, restaurants, and other similar places, where the system continuously checks whether customers are adhering to social distancing norms and only allows a certain number of people into the commercial space. This system is made up of two parts: an Entry Control System and a Six feet Apart analysis. This paper's work has been compared to previously completed projects and discussed. People who are concerned about social distancing and overcrowding will benefit greatly from the installation of this gadget in the private and/or public sectors.

Indexed as

Deep LearningDistance MeasurementEntry Control SystemObject DetectionObject TrackingSix Feet Apart Analysis

Identifiers

PMID35291397
PMCPMC8914536

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.