# Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition

arXiv: https://arxiv.org/abs/2205.11394 · HTML: https://mlsanity.com/paper/2205.11394/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 16 of 16)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection](https://mlsanity.com/paper/1903.07256/) (2019)
- [MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection](https://mlsanity.com/paper/2104.01633/) (2021)
- [Large-scale weakly-supervised pre-training for video action recognition](https://mlsanity.com/paper/1905.00561/) (2019)
- [SMART Frame Selection for Action Recognition](https://mlsanity.com/paper/2012.10671/) (2020)
- [Localizing Anomalies from Weakly-Labeled Videos](https://mlsanity.com/paper/2008.08944/) (2020)
- [Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video](https://mlsanity.com/paper/2108.03825/) (2021)
- [No frame left behind: Full Video Action Recognition](https://mlsanity.com/paper/2103.15395/) (2021)
- [Learning to Detect Violent Videos using Convolutional Long Short-Term Memory](https://mlsanity.com/paper/1709.06531/) (2017)
- [Anomaly Locality in Video Surveillance](https://mlsanity.com/paper/1901.10364/) (2019)
- [Vision-based Fight Detection from Surveillance Cameras](https://mlsanity.com/paper/2002.04355/) (2020)

## Built on by (most cited first, 2 of 2)

- [Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model](https://mlsanity.com/paper/2209.11477/) (2022)
- [A Novel Wide-Area Multiobject Detection System with High-Probability Region Searching](https://mlsanity.com/paper/2405.04589/) (2024)

## Abstract (arXiv)

Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type of anomaly detection and as binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets for most of the study since they have frame-level annotations. We find that the anomaly detection has similar or even better performance than the action recognition. Furthermore, we study to use anomaly detection as a toolbox to generate training datasets for action recognition in an iterative way conditioned on the performance of the anomaly detection. Experiment results should show that we achieve state-of-the-art performance on three fight detection datasets.