Evidence map›Paper›PMID 35890950›Full record

ArticleSensors (Basel, Switzerland)2022

Tracking Missing Person in Large Crowd Gathering Using Intelligent Video Surveillance.

Adnan Nadeem, Muhammad Ashraf, Nauman Qadeer, Kashif Rizwan, Amir Mehmood, Ali AlZahrani, Fazal Noor, Qammer H Abbasi

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
2.0field-weighted citation impact, top 12% of its field
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

0 citing papers in PubMed, 21 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 4 institutions in 3 countries.

Adnan NadeemFaculty of Computer and Information System, Islamic University of Madinah, Madinah 42351, Saudi Arabia.ORCID 0000-0001-9622-150X
Muhammad AshrafDepartment of Physics, Federal Urdu University of Arts, Science & Technology, Karachi 75300, Pakistan.ORCID 0000-0002-3437-1796
Nauman QadeerDepartment of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad 45570, Pakistan.ORCID 0000-0002-2178-0435
Kashif RizwanDepartment of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad 45570, Pakistan.ORCID 0000-0002-5595-3867
Amir MehmoodDepartment of Computer Science and Information Technology, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan.ORCID 0000-0001-8689-8015
Ali AlZahraniFaculty of Computer and Information System, Islamic University of Madinah, Madinah 42351, Saudi Arabia.ORCID 0000-0001-9501-8331
Fazal NoorFaculty of Computer and Information System, Islamic University of Madinah, Madinah 42351, Saudi Arabia.ORCID 0000-0002-0096-3435
Qammer H AbbasiJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.ORCID 0000-0002-7097-9969
Federal Urdu University · PKIslamic University of Madinah · SASir Syed University of Engineering and Technology · PKUniversity of Glasgow · GB

Funding

Islamic University of Madinah 1442/505
6 · The paper itself

Abstract

Locating a missing child or elderly person in a large gathering through face recognition in videos is still challenging because of various dynamic factors. In this paper, we present an intelligent mechanism for tracking missing persons in an unconstrained large gathering scenario of Al-Nabawi Mosque, Madinah, KSA. The proposed mechanism in this paper is unique in two aspects. First, there are various proposals existing in the literature that deal with face detection and recognition in high-quality images of a large crowd but none of them tested tracking of a missing person in low resolution images of a large gathering scenario. Secondly, our proposed mechanism is unique in the sense that it employs four phases: (a) report missing person online through web and mobile app based on spatio-temporal features; (b) geo fence set estimation for reducing search space; (c) face detection using the fusion of Viola Jones cascades LBP, CART, and HAAR to optimize the results of the localization of face regions; and (d) face recognition to find a missing person based on the profile image of reported missing person. The overall results of our proposed intelligent tracking mechanism suggest good performance when tested on a challenging dataset of 2208 low resolution images of large crowd gathering.

Indexed as

AlgorithmsPattern Recognition, AutomatedAgedChildCrowdingFaceHumansRecognition, Psychologyfaces detectionfacial recognitionintelligent video surveillancelarge crowd gatheringmissing persons trackingspatio-temporal featuresunconstrained environmentViola Jones cascades fusion

Identifiers

PMID35890950
PMCPMC9323688
OpenAlexW4285595126

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.