# Learning to Detect Violent Videos using Convolutional Long Short-Term Memory

arXiv: https://arxiv.org/abs/1709.06531 · HTML: https://mlsanity.com/paper/1709.06531/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 23 of 23)

- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM](https://mlsanity.com/paper/2102.10590/) (2021)
- [Video Vision Transformers for Violence Detection](https://mlsanity.com/paper/2209.03561/) (2022)
- [Self-supervised Learning for Unintentional Action Prediction](https://mlsanity.com/paper/2209.12074/) (2022)
- [Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model](https://mlsanity.com/paper/2202.02212/) (2022)
- [An Overview of Violence Detection Techniques: Current Challenges and Future Directions](https://mlsanity.com/paper/2209.11680/) (2022)
- [Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition](https://mlsanity.com/paper/2205.11394/) (2022)
- [SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence](https://mlsanity.com/paper/2207.12850/) (2022)
- [Zero-shot Visual Commonsense Immorality Prediction](https://mlsanity.com/paper/2211.05521/) (2022)
- [Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures](https://mlsanity.com/paper/2308.05106/) (2023)
- [2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos](https://mlsanity.com/paper/2409.07588/) (2024)
- [Real-Time Violence Detection Using CNN-LSTM](https://mlsanity.com/paper/2107.07578/) (2021)
- [PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos](https://mlsanity.com/paper/2109.05695/) (2021)
- [Violence detection in videos using deep recurrent and convolutional neural networks](https://mlsanity.com/paper/2409.07581/) (2024)
- [Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos](https://mlsanity.com/paper/2504.00857/) (2025)
- [AVAR-Net: A Lightweight Audio-Visual Anomaly Recognition Framework with a Benchmark Dataset](https://mlsanity.com/paper/2510.13630/) (2025)
- [Fight Detection from Still Images in the Wild](https://mlsanity.com/paper/2111.08370/) (2021)
- [Detecting Violence in Video Based on Deep Features Fusion Technique](https://mlsanity.com/paper/2204.07443/) (2022)
- [Two-stream Multi-dimensional Convolutional Network for Real-time Violence Detection](https://mlsanity.com/paper/2211.04255/) (2022)
- [DIFEM: Key-points Interaction based Feature Extraction Module for Violence Recognition in Videos](https://mlsanity.com/paper/2412.05386/) (2024)
- [Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants](https://mlsanity.com/paper/2503.19146/) (2025)
- [Vi-SAFE: A Spatial-Temporal Framework for Efficient Violence Detection in Public Surveillance](https://mlsanity.com/paper/2509.13210/) (2025)
- [Towards Adaptive Fusion of Multimodal Deep Networks for Human Action Recognition](https://mlsanity.com/paper/2512.04943/) (2025)

## Abstract (arXiv)

Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos. A convolutional neural network is used to extract frame level features from a video. The frame level features are then aggregated using a variant of the long short term memory that uses convolutional gates. The convolutional neural network along with the convolutional long short term memory is capable of capturing localized spatio-temporal features which enables the analysis of local motion taking place in the video. We also propose to use adjacent frame differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance of the proposed feature extraction pipeline is evaluated on three standard benchmark datasets in terms of recognition accuracy. Comparison of the results obtained with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.