Zooming into Abnormal Events in Video Conferencing

Shmuel Horowitz; Dima Kagan; Galit Fuhrmann Alpert; Michael Fire

doi:10.1109/BigData59044.2023.10386627

Zooming into Abnormal Events in Video Conferencing

Shmuel Horowitz, Dima Kagan, Galit Fuhrmann Alpert, Michael Fire

Department of Software and Information Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Video conferencing (VC) has become increasingly popular, bringing new challenges in privacy and security, one notable example is of Zoombombing. Furthermore, other issues related to VC usage have emerged, such as keeping students involved. Identifying abnormal segments in VC meetings in vast data is a challenging task. Here, we introduce a novel algorithm to detect such anomalies in VC automatically. By analyzing publicly available VC recordings, our algorithm tracks and analyzes changes in participants' facial expressions to identify and quantity overall meeting climate changes. We demonstrate performance of 92.3% precision in anomaly detection on the collected dataset. Our model offers a pioneering solution for recognizing abnormal events in VC meetings.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers
Pages	1330-1335
Number of pages	6
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386627
State	Published - 1 Jan 2023
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Keywords

Anomaly detection in video
Video conference

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/BigData59044.2023.10386627

Cite this

Horowitz, S., Kagan, D., Alpert, G. F., & Fire, M. (2023). Zooming into Abnormal Events in Video Conferencing. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 1330-1335). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData59044.2023.10386627

Horowitz, Shmuel ; Kagan, Dima ; Alpert, Galit Fuhrmann et al. / Zooming into Abnormal Events in Video Conferencing. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers, 2023. pp. 1330-1335 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

@inproceedings{021aefa484864f6cb681897ef6573f38,

title = "Zooming into Abnormal Events in Video Conferencing",

abstract = "Video conferencing (VC) has become increasingly popular, bringing new challenges in privacy and security, one notable example is of Zoombombing. Furthermore, other issues related to VC usage have emerged, such as keeping students involved. Identifying abnormal segments in VC meetings in vast data is a challenging task. Here, we introduce a novel algorithm to detect such anomalies in VC automatically. By analyzing publicly available VC recordings, our algorithm tracks and analyzes changes in participants' facial expressions to identify and quantity overall meeting climate changes. We demonstrate performance of 92.3% precision in anomaly detection on the collected dataset. Our model offers a pioneering solution for recognizing abnormal events in VC meetings.",

keywords = "Anomaly detection in video, Video conference",

author = "Shmuel Horowitz and Dima Kagan and Alpert, {Galit Fuhrmann} and Michael Fire",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

month = jan,

day = "1",

doi = "10.1109/BigData59044.2023.10386627",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "1330--1335",

editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, {Jerry Chun-Wei} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

address = "United States",

}

Horowitz, S, Kagan, D, Alpert, GF & Fire, M 2023, Zooming into Abnormal Events in Video Conferencing. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers, pp. 1330-1335, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386627

Zooming into Abnormal Events in Video Conferencing. / Horowitz, Shmuel; Kagan, Dima; Alpert, Galit Fuhrmann et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers, 2023. p. 1330-1335 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Zooming into Abnormal Events in Video Conferencing

AU - Horowitz, Shmuel

AU - Kagan, Dima

AU - Alpert, Galit Fuhrmann

AU - Fire, Michael

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Video conferencing (VC) has become increasingly popular, bringing new challenges in privacy and security, one notable example is of Zoombombing. Furthermore, other issues related to VC usage have emerged, such as keeping students involved. Identifying abnormal segments in VC meetings in vast data is a challenging task. Here, we introduce a novel algorithm to detect such anomalies in VC automatically. By analyzing publicly available VC recordings, our algorithm tracks and analyzes changes in participants' facial expressions to identify and quantity overall meeting climate changes. We demonstrate performance of 92.3% precision in anomaly detection on the collected dataset. Our model offers a pioneering solution for recognizing abnormal events in VC meetings.

AB - Video conferencing (VC) has become increasingly popular, bringing new challenges in privacy and security, one notable example is of Zoombombing. Furthermore, other issues related to VC usage have emerged, such as keeping students involved. Identifying abnormal segments in VC meetings in vast data is a challenging task. Here, we introduce a novel algorithm to detect such anomalies in VC automatically. By analyzing publicly available VC recordings, our algorithm tracks and analyzes changes in participants' facial expressions to identify and quantity overall meeting climate changes. We demonstrate performance of 92.3% precision in anomaly detection on the collected dataset. Our model offers a pioneering solution for recognizing abnormal events in VC meetings.

KW - Anomaly detection in video

KW - Video conference

UR - http://www.scopus.com/inward/record.url?scp=85184982228&partnerID=8YFLogxK

U2 - 10.1109/BigData59044.2023.10386627

DO - 10.1109/BigData59044.2023.10386627

M3 - Conference contribution

AN - SCOPUS:85184982228

T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

SP - 1330

EP - 1335

BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

A2 - Palpanas, Themis

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Dou, Dejing

A2 - Slezak, Dominik

A2 - Wang, Wei

A2 - Gruca, Aleksandra

A2 - Lin, Jerry Chun-Wei

A2 - Agrawal, Rakesh

PB - Institute of Electrical and Electronics Engineers

T2 - 2023 IEEE International Conference on Big Data, BigData 2023

Y2 - 15 December 2023 through 18 December 2023

ER -

Horowitz S, Kagan D, Alpert GF , Fire M. Zooming into Abnormal Events in Video Conferencing. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers. 2023. p. 1330-1335. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386627

Zooming into Abnormal Events in Video Conferencing

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this