Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization.

Nadia Mumtaz, Naveed Ejaz, Suliman Aladhadh, Shabana Habib, Mi Young Lee

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. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

2.3field-weighted citation impact, top 10% 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

1 citing paper in PubMed, 20 citations in OpenAlex.

Literature Review of Deep-Learning-Based Detection of Violence in Video.Sensors (Basel, Switzerland) · 2024
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

5 authors at 3 institutions in 4 countries.

Nadia MumtazDepartment of Computing and Technology, Iqra University, Islamabad Campus, Islamabad 44000, Pakistan.

Naveed EjazDepartment of Computing and Technology, Iqra University, Islamabad Campus, Islamabad 44000, Pakistan.ORCID 0000-0003-1295-4787

Suliman AladhadhDepartment of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia.ORCID 0000-0003-4349-8710

Shabana HabibDepartment of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia.ORCID 0000-0002-6543-2520

Mi Young LeeDepartment of Software, Sejong University, Seoul 05006, Republic of Korea.

Iqra University · PKQassim University · SASejong University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analysis and instant decision making using efficient computer vision algorithms need researchers' attentions. In this research, to the best of our knowledge, we are the first to introduce a process control technique:

Indexed as

AlgorithmsHumansanomaly detectionfight detectionsurveillance video analysisvideo classification

Identifiers

PMID36502084

DOI10.3390/s22239383

PMCPMC9739222

OpenAlexW4311104044

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.