# Video Vision Transformers for Violence Detection

arXiv: https://arxiv.org/abs/2209.03561 · HTML: https://mlsanity.com/paper/2209.03561/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://mlsanity.com/paper/2106.10270/) (2021)
- [Learning to Detect Violent Videos using Convolutional Long Short-Term Memory](https://mlsanity.com/paper/1709.06531/) (2017)
- [Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework](https://mlsanity.com/paper/2209.01401/) (2022)

## Built on by (most cited first, 8 of 8)

- [MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili](https://mlsanity.com/paper/2408.03468/) (2024)
- [MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos](https://mlsanity.com/paper/2304.05292/) (2023)
- [Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI](https://mlsanity.com/paper/2305.14389/) (2023)
- [4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model](https://mlsanity.com/paper/2604.22700/) (2026)
- [Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition](https://mlsanity.com/paper/2305.00552/) (2023)
- [Confidence Aware SSD Ensemble with Weighted Boxes Fusion for Weapon Detection](https://mlsanity.com/paper/2509.23697/) (2025)
- [Towards Training-free Multimodal Hate Localisation with Large Language Models](https://mlsanity.com/paper/2602.09637/) (2026)
- [Video-Based Sexual Harassment Detection Using Fine-Tuned Video Transformers](https://mlsanity.com/paper/2610.15354/) (2026)

## Abstract (arXiv)

Law enforcement and city safety are significantly impacted by detecting violent incidents in surveillance systems. Although modern (smart) cameras are widely available and affordable, such technological solutions are impotent in most instances. Furthermore, personnel monitoring CCTV recordings frequently show a belated reaction, resulting in the potential cause of catastrophe to people and property. Thus automated detection of violence for swift actions is very crucial. The proposed solution uses a novel end-to-end deep learning-based video vision transformer (ViViT) that can proficiently discern fights, hostile movements, and violent events in video sequences. The study presents utilizing a data augmentation strategy to overcome the downside of weaker inductive biasness while training vision transformers on a smaller training datasets. The evaluated results can be subsequently sent to local concerned authority, and the captured video can be analyzed. In comparison to state-of-theart (SOTA) approaches the proposed method achieved auspicious performance on some of the challenging benchmark datasets.