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Review

Literature Review of Deep-Learning-Based Detection of Violence in Video

by
Pablo Negre
1,*,
Ricardo S. Alonso
2,3,
Alfonso González-Briones
1,
Javier Prieto
1 and
Sara Rodríguez-González
1
1
BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain
2
AIR Institute, Av. Santiago Madrigal, 37008 Salamanca, Spain
3
UNIR (International University of La Rioja), Av. de la Paz, 137, 26006 Logroño, Spain
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(12), 4016; https://doi.org/10.3390/s24124016
Submission received: 17 May 2024 / Revised: 18 June 2024 / Accepted: 19 June 2024 / Published: 20 June 2024
(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)
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Abstract

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.
Keywords: video violence detection; artificial intelligence; surveillance camera; action recognition; computer vision video violence detection; artificial intelligence; surveillance camera; action recognition; computer vision

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MDPI and ACS Style

Negre, P.; Alonso, R.S.; González-Briones, A.; Prieto, J.; Rodríguez-González, S. Literature Review of Deep-Learning-Based Detection of Violence in Video. Sensors 2024, 24, 4016. https://doi.org/10.3390/s24124016

AMA Style

Negre P, Alonso RS, González-Briones A, Prieto J, Rodríguez-González S. Literature Review of Deep-Learning-Based Detection of Violence in Video. Sensors. 2024; 24(12):4016. https://doi.org/10.3390/s24124016

Chicago/Turabian Style

Negre, Pablo, Ricardo S. Alonso, Alfonso González-Briones, Javier Prieto, and Sara Rodríguez-González. 2024. "Literature Review of Deep-Learning-Based Detection of Violence in Video" Sensors 24, no. 12: 4016. https://doi.org/10.3390/s24124016

APA Style

Negre, P., Alonso, R. S., González-Briones, A., Prieto, J., & Rodríguez-González, S. (2024). Literature Review of Deep-Learning-Based Detection of Violence in Video. Sensors, 24(12), 4016. https://doi.org/10.3390/s24124016

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