{"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/data-efficient-video-transformer-for-violence","title":"Data Efficient Video Transformer for Violence Detection","arxiv_id":null,"date":"2021-07-17","proceeding":"IEEE International Conference on Communication, Networks and Satellite (ComNetSat) 2021 7","authors":["almamon rasool abdali"],"abstract":"In smart cities, violence event detection is critical to ensure city safety. Several studies have been done on this topic with a focus on 2d-Convolutional Neural Network (2d-CNN) to detect spatial features from each frame, followed by one of the Recurrent Neural Networks (RNN) variants as a temporal features learning method. On the other hand, the transformer network has achieved a great result in many areas. The bottleneck for transformers is the need for large data set to achieve good results. In this work, we propose a data-efficient video transformer (DeVTr) based on the transformer network as a Spatio-temporal learning method with a pre-trained 2d-Convolutional neural network (2d-CNN) as an embedding layer for the input data. The model has been trained and tested on the Real-life violence dataset (RLVS) and achieved an accuracy of 96.25%. A comparison of the result for the suggested method with previous techniques illustrated that the suggested method provides the best result among all the other studies for violence event detection.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9530829","url_pdf":"https://ieeexplore.ieee.org/abstract/document/9530829","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":[{"paper_slug":"data-efficient-video-transformer-for-violence","repo_url":"https://github.com/mamonraab/Data-efficient-video-transformer","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"event-detection","task_name":"Event Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-recognition-on-real-life-violence","task":"Action Recognition","dataset":"Real Life Violence Situations Dataset","model":"DeVTr","rank_in_archive_order":1,"of":3,"metrics":{"accuracy":"96.25%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}