Violence Detection Using Computer Vision Approaches

Talha, Khalid Raihan; Bandapadya, Koushik; Khan, Mohammad Monirujjaman

doi:10.1109/aiiot54504.2022.9817374

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…CNNs extract spatial features, and these features are fed as input to the LSTM to extract temporal features. There are two distinct phases, with the LSTM being fed from the features extracted by the CNN and the LSTM [35,36]. • CNN: Articles that rely exclusively on CNNs for violence detection in videos.…”

Section: Selected Article Descriptionmentioning

confidence: 99%

“…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%

“…Talha et al [ 35 ] developed a very fast and efficient real-time violence detection system, which was tested on the personal devices of the system developers. It is based on a CNN which extracts spatial features.…”

Section: Selected Article Descriptionmentioning

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%

See 3 more Smart Citations

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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Section: Selected Article Descriptionmentioning

confidence: 99%