# Detecting Violence in Video Based on Deep Features Fusion Technique

arXiv: https://arxiv.org/abs/2204.07443 · HTML: https://mlsanity.com/paper/2204.07443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [Going Deeper into Action Recognition: A Survey](https://mlsanity.com/paper/1605.04988/) (2016)
- [Learning to Detect Violent Videos using Convolutional Long Short-Term Memory](https://mlsanity.com/paper/1709.06531/) (2017)
- [Learning a Deep Model for Human Action Recognition from Novel Viewpoints](https://mlsanity.com/paper/1602.00828/) (2016)
- [ReHAR: Robust and Efficient Human Activity Recognition](https://mlsanity.com/paper/1802.09745/) (2018)

## Abstract (arXiv)

With the rapid growth of surveillance cameras in many public places to mon-itor human activities such as in malls, streets, schools and, prisons, there is a strong demand for such systems to detect violence events automatically. Au-tomatic analysis of video to detect violence is significant for law enforce-ment. Moreover, it helps to avoid any social, economic and environmental damages. Mostly, all systems today require manual human supervisors to de-tect violence scenes in the video which is inefficient and inaccurate. in this work, we interest in physical violence that involved two persons or more. This work proposed a novel method to detect violence using a fusion tech-nique of two significantly different convolutional neural networks (CNNs) which are AlexNet and SqueezeNet networks. Each network followed by separate Convolution Long Short Term memory (ConvLSTM) to extract ro-bust and richer features from a video in the final hidden state. Then, making a fusion of these two obtained states and fed to the max-pooling layer. Final-ly, features were classified using a series of fully connected layers and soft-max classifier. The performance of the proposed method is evaluated using three standard benchmark datasets in terms of detection accuracy: Hockey Fight dataset, Movie dataset and Violent Flow dataset. The results show an accuracy of 97%, 100%, and 96% respectively. A comparison of the results with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.