Multimodal Violent Video Recognition Based on Mutual Distillation

Wang, Xiaoyu; Li, Rui

doi:10.1007/978-3-031-18913-5_48

Cited by 3 publications

(2 citation statements)

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“…Shang et al [ 83 ] divided the related work into deep learning models, supervised and unsupervised learning, knowledge transfer (knowledge distillation), and multimodal learning. Firstly, the authors proposed to transfer information from large datasets to small violent datasets based on mutual distillation with a pre-trained self-supervised model for RGB vital features.…”

Section: Selected Article Descriptionmentioning

confidence: 99%

“…The dataset developed by Rachna et al [ 105 ] contains YouTube videos, stock films, and self-recorded violent videos. The Violent Clip Dataset (VCD) [ 83 ] contains 37 Hollywood movies and 30 YouTube video clips. The dataset created by Hung et al [ 42 ] includes volunteers acting out different scenarios, such as elderly patients with lower limb disabilities.…”

Section: Description Of the Analysis Of The Selected Itemsmentioning

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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Section: Selected Article Descriptionmentioning

confidence: 99%

Section: Description Of the Analysis Of The Selected Itemsmentioning

confidence: 99%