Deep Learning-Based Violence Detection from Videos

Singh, Neha; Prasad, Onkareshwar; Sujithra, T.

doi:10.1007/978-981-16-6624-7_32

Cited by 1 publication

(4 citation statements)

References 11 publications

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“…Thus, it has been possible to observe the most used algorithms in phases 1 and 2 of the process of detecting violence in videos, as well as the most used combinations. [29,35,39,43,45,49,50,53,[56][57][58][59][60][62][63][64]66,71,74,76,77,[86][87][88]92].…”

Section: Violence Detection Algorithmsmentioning

confidence: 99%

“…Sharma et al [ 59 ] used a CNN pre-trained with ImageNet for spatial feature extraction called Xception, the output of which is fed into an LSTM. Singh et al [ 60 ] separated the state of the art between motion-based, machine learning, and deep learning. They used two CNNs to extract low-level features and local motion features.…”

Section: Selected Article Descriptionmentioning

confidence: 99%

“…Hung et al [ 42 ] used OpenPose, which is a library and a set of deep learning models used for human body keypoint detection. Singh et al [ 60 ] used OpenCV (Open Source Computer Vision Library), which is a widely used open-source library in computer vision that provides a wide range of tools and functions to perform tasks such as object detection and object tracking. Naik et al [ 89 ] and Ullah et al [ 48 ] used Mask-RCNN, which is a convolutional neural network architecture used for object detection and semantic segmentation in images.…”

Section: Description Of the Analysis Of The Selected Itemsmentioning

confidence: 99%

“…Colors: Yellow corresponds to the use of CNNs, purple to skeleton-based algorithms, orange to manual techniques, red to those utilizing audio, gray to those employing transformers, and green to LSTM. Citations in descending order of accuracy: [ 29 , 35 , 39 , 43 , 45 , 49 , 50 , 53 , 56 , 57 , 58 , 59 , 60 , 62 , 63 , 64 , 66 , 71 , 74 , 76 , 77 , 86 , 87 , 88 , 92 ].…”

Section: Figurementioning

confidence: 99%

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

Negre,

Alonso,

González-Briones

et al. 2024

Sensors

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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.

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