short-paper

A Crowd Analysis Framework for Detecting Violence Scenes

Authors:

Konstantinos Gkountakos,

Konstantinos Ioannidis,

Theodora Tsikrika,

Stefanos Vrochidis,

Ioannis KompatsiarisAuthors Info & Claims

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

Pages 276 - 280

https://doi.org/10.1145/3372278.3390725

Published: 08 June 2020 Publication History

Abstract

This work examines violence detection in video scenes of crowds and proposes a crowd violence detection framework based on a 3D convolutional deep learning architecture, the 3D-ResNet model with 50 layers. The proposed framework is evaluated on the Violent Flows dataset against several state-of-the-art approaches and achieves higher accuracy values in almost all cases, while also performing the violence detection activities in (near) real-time.

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Cited By

Negre PAlonso RGonzález-Briones APrieto JRodríguez-González S(2024)Literature Review of Deep-Learning-Based Detection of Violence in VideoSensors10.3390/s2412401624:12(4016)Online publication date: 20-Jun-2024
https://doi.org/10.3390/s24124016
Negre PAlonso RPrieto JDang CCorchado J(2024)Systematic Mapping Study on Violence Detection in Video by Means of Trustworthy Artificial IntelligenceSSRN Electronic Journal10.2139/ssrn.4757631Online publication date: 2024
https://doi.org/10.2139/ssrn.4757631
Wu WYi WWang XZheng X(2024)A Vision-Driven Model Based on Cognitive Heuristics for Simulating Subgroup Behaviors During EvacuationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.342162625:11(16048-16058)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3421626
Show More Cited By

Index Terms

A Crowd Analysis Framework for Detecting Violence Scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene anomaly detection
        Scene understanding

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Information

Published In

cover image ACM Conferences

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

June 2020

605 pages

ISBN:9781450370875

DOI:10.1145/3372278

General Chairs:
Cathal Gurrin
Dublin City University, Ireland
,
Björn Þór Jónsson
IT University of Copenhagen, Denmark
,
Noriko Kando
National Institute of Informatics, Tokyo
,
Program Chairs:
Klaus Schoeffmann
Klagenfurt University, Austria
,
Phoebe Chen
La Trobe University, Australia
,
Noel E. O'Connor
Dublin City University, Ireland

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2020

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Short-paper

Funding Sources

European Commission

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ICMR '20

Sponsor:

SIGMM

ICMR '20: International Conference on Multimedia Retrieval

June 8 - 11, 2020

Dublin, Ireland

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Negre PAlonso RGonzález-Briones APrieto JRodríguez-González S(2024)Literature Review of Deep-Learning-Based Detection of Violence in VideoSensors10.3390/s2412401624:12(4016)Online publication date: 20-Jun-2024
https://doi.org/10.3390/s24124016
Negre PAlonso RPrieto JDang CCorchado J(2024)Systematic Mapping Study on Violence Detection in Video by Means of Trustworthy Artificial IntelligenceSSRN Electronic Journal10.2139/ssrn.4757631Online publication date: 2024
https://doi.org/10.2139/ssrn.4757631
Wu WYi WWang XZheng X(2024)A Vision-Driven Model Based on Cognitive Heuristics for Simulating Subgroup Behaviors During EvacuationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.342162625:11(16048-16058)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3421626
Gkountakos KIoannidis KDemestichas KVrochidis SKompatsiaris I(2024)A Comprehensive Review of Deep Learning-Based Anomaly Detection Methods for Precision AgricultureIEEE Access10.1109/ACCESS.2024.352224812(197715-197733)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3522248
G. SM.A. S(2024)Violence detection in crowd videos using nuanced facial expression analysisSystems and Soft Computing10.1016/j.sasc.2024.2001046(200104)Online publication date: Dec-2024
https://doi.org/10.1016/j.sasc.2024.200104
Ganga BB.T. LK.R. V(2024)Object detection and crowd analysis using deep learning techniques: Comprehensive review and future directionsNeurocomputing10.1016/j.neucom.2024.127932597(127932)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.127932
Theodosiadou OChatzakou DTsikrika TVrochidis SKompatsiaris I(2023)Real‐time threat assessment based on hidden Markov modelsRisk Analysis10.1111/risa.1410543:10(2069-2081)Online publication date: Feb-2023
https://doi.org/10.1111/risa.14105
Jaafar NLachiri Z(2023)Multimodal fusion methods with deep neural networks and meta-information for aggression detection in surveillanceExpert Systems with Applications10.1016/j.eswa.2022.118523211(118523)Online publication date: Jan-2023
https://doi.org/10.1016/j.eswa.2022.118523
Ren GLu XLi Y(2022)Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency AnalysisJournal of Sensors10.1155/2022/72477572022(1-19)Online publication date: 12-Aug-2022
https://doi.org/10.1155/2022/7247757
Rahman MRahman RSupty KSabah RIslam MIslam MAhmed N(2022)A Real Time Abysmal Activity Detection System Towards the Enhancement of Road Safety2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)10.1109/IRASET52964.2022.9738165(1-5)Online publication date: 3-Mar-2022
https://doi.org/10.1109/IRASET52964.2022.9738165
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