# Vision-based Fight Detection from Surveillance Cameras

arXiv: https://arxiv.org/abs/2002.04355 · HTML: https://mlsanity.com/paper/2002.04355/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model](https://mlsanity.com/paper/2209.11477/) (2022)
- [Multimedia Datasets for Anomaly Detection: A Review](https://mlsanity.com/paper/2112.05410/) (2021)
- [Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition](https://mlsanity.com/paper/2205.11394/) (2022)
- [JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos](https://mlsanity.com/paper/2405.02961/) (2024)
- [FuseMamba-VD: Dual Branch VideoMamba with Gated Class Token Fusion for Violence Detection](https://mlsanity.com/paper/2506.03162/) (2025)
- [Fight Detection from Still Images in the Wild](https://mlsanity.com/paper/2111.08370/) (2021)

## Abstract (arXiv)

Vision-based action recognition is one of the most challenging research topics of computer vision and pattern recognition. A specific application of it, namely, detecting fights from surveillance cameras in public areas, prisons, etc., is desired to quickly get under control these violent incidents. This paper addresses this research problem and explores LSTM-based approaches to solve it. Moreover, the attention layer is also utilized. Besides, a new dataset is collected, which consists of fight scenes from surveillance camera videos available at YouTube. This dataset is made publicly available. From the extensive experiments conducted on Hockey Fight, Peliculas, and the newly collected fight datasets, it is observed that the proposed approach, which integrates Xception model, Bi-LSTM, and attention, improves the state-of-the-art accuracy for fight scene classification.