Violence Recognition from Videos using Deep Learning Techniques
Soliman, Mohamed Mostafa; Kamal, Mohamed Hussein; El-Massih Nashed, Mina Abd; Mostafa, Youssef Mohamed; Chawky, Bassel Safwat; Khattab, Dina;
Abstract
Automatic recognition of violence between individuals or crowds in videos has a broad interest. In this work, an end-to-end deep neural network model for the purpose of recognizing violence in videos is proposed. The proposed model uses a pre-trained VGG-16 on ImageNet as spatial feature extractor followed by Long Short-Term Memory (LSTM) as temporal feature extractor and sequence of fully connected layers for classification purpose. The achieved accuracy is near state-of-the-art. Also, we contribute by introducing a new benchmark called Real- Life Violence Situations which contains 2000 short videos divided into 1000 violence videos and 1000 non-violence videos. The new benchmark is used for fine-tuning the proposed models achieving a best accuracy of 88.2%.
Other data
Title | Violence Recognition from Videos using Deep Learning Techniques | Authors | Soliman, Mohamed Mostafa; Kamal, Mohamed Hussein; El-Massih Nashed, Mina Abd; Mostafa, Youssef Mohamed; Chawky, Bassel Safwat; Khattab, Dina | Keywords | deep learning;LSTM;violence recognition;VGG-16 | Issue Date | 1-Dec-2019 | Journal | Proceedings - 2019 IEEE 9th International Conference on Intelligent Computing and Information Systems, ICICIS 2019 | Start page | 79 | End page | 84 | ISBN | 9781728139951 | DOI | 10.1109/ICICIS46948.2019.9014714 | Scopus ID | 2-s2.0-85083382692 |
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