{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/violence-recognition-from-videos-using-deep","title":"Violence Recognition from Videos using Deep Learning Techniques","arxiv_id":null,"date":"2019-12-10","proceeding":"IEEE International Conference on Intelligent Computing and Information Systems (ICICIS) 2019 12","authors":["Mohamed Mostafa Soliman","Mohamed Hussein Kamal","Mina Abd El-Massih Nashed","Youssef Mohamed Mostafa","Bassel Safwat Chawky","Dina Khattab"],"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%.","url_abs":"https://ieeexplore.ieee.org/document/9014714","url_pdf":"https://ieeexplore.ieee.org/document/9014714","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-recognition-on-real-life-violence","task":"Action Recognition","dataset":"Real Life Violence Situations Dataset","model":"CNN+LSTM","rank_in_archive_order":3,"of":3,"metrics":{"accuracy":"88.8%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}