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198640

Comparative Study for Anomaly Detection in Crowded Scenes

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Last updated: 03 Jan 2025

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Tags

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Abstract

Nowadays, video analysis is an important research area especially from a security point of view. The discovery of unusual activities is important because it is a difficult task for humans especially with increasing number of surveillance cameras in all crowded places. That is because it requires a lot of human effort, and these activities happen rarely. Also the definition of anomaly events is different based on the location of the event. For example running in the park is a normal event but running in a restaurant is an abnormal event. The event is the same but the place was the factor of making it normal or not. The main objective of this paper is to compile what has been achieved in the field of anomaly detection and compare them, and to look at the different datasets used in the recent period. We will show how to detect and identify anomalies in videos, approaches for video anomaly detection and also what are the latest learning frameworks.

DOI

10.21608/ijicis.2021.84588.1112

Keywords

Abnormal event detection, Video surveillance, unsupervised learning

Authors

First Name

Mohamed

Last Name

Abdelghafour

MiddleName

-

Affiliation

Computer Science department, Computer and Information Science, Ain Shams University, Cairo, Egypt

Email

mohamed.abdelghafour@cis.asu.edu.eg

City

Cairo

Orcid

0000-0001-9714-4438

First Name

Maryam

Last Name

ElBery

MiddleName

-

Affiliation

Scientific Computing department, Computer and Information Science, Ain Shams University, Cairo, Egypt

Email

maryam_nabil@cis.asu.edu.eg

City

-

Orcid

0000-0001-7424-5869

First Name

Zaki

Last Name

Taha

MiddleName

-

Affiliation

Computer Science department, Computer and Information Science, Ain Shams University, Cairo, Egypt

Email

ztfayed@hotmail.com

City

-

Orcid

-

Volume

21

Article Issue

3

Related Issue

28630

Issue Date

2021-11-01

Receive Date

2021-07-07

Publish Date

2021-11-01

Page Start

84

Page End

94

Print ISSN

1687-109X

Online ISSN

2535-1710

Link

https://ijicis.journals.ekb.eg/article_198640.html

Detail API

https://ijicis.journals.ekb.eg/service?article_code=198640

Order

15

Type

Original Article

Type Code

494

Publication Type

Journal

Publication Title

International Journal of Intelligent Computing and Information Sciences

Publication Link

https://ijicis.journals.ekb.eg/

MainTitle

Comparative Study for Anomaly Detection in Crowded Scenes

Details

Type

Article

Created At

22 Jan 2023