{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:00:13Z","timestamp":1779202813835,"version":"3.51.4"},"reference-count":106,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretary of State for Digitalization and Artificial Intelligence"},{"name":"European Union (Next Generation)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.<\/jats:p>","DOI":"10.3390\/s24124016","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T03:44:49Z","timestamp":1718941489000},"page":"4016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Literature Review of Deep-Learning-Based Detection of Violence in Video"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8372-7572","authenticated-orcid":false,"given":"Pablo","family":"Negre","sequence":"first","affiliation":[{"name":"BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6599-0186","authenticated-orcid":false,"given":"Ricardo S.","family":"Alonso","sequence":"additional","affiliation":[{"name":"AIR Institute, Av. Santiago Madrigal, 37008 Salamanca, Spain"},{"name":"UNIR (International University of La Rioja), Av. de la Paz, 137, 26006 Logro\u00f1o, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3444-4393","authenticated-orcid":false,"given":"Alfonso","family":"Gonz\u00e1lez-Briones","sequence":"additional","affiliation":[{"name":"BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8175-2201","authenticated-orcid":false,"given":"Javier","family":"Prieto","sequence":"additional","affiliation":[{"name":"BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3081-5177","authenticated-orcid":false,"given":"Sara","family":"Rodr\u00edguez-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"BISITE Research Group, Universidad de Salamanca, Patio de Escuelas, 37008 Salamanca, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103223","DOI":"10.1016\/j.cities.2021.103223","article-title":"Ambient population and surveillance cameras: The guardianship role in street robbers\u2019 crime location choice","volume":"115","author":"Long","year":"2021","journal-title":"Cities"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Muarifah, A., Mashar, R., Hashim, I.H.M., Rofiah, N.H., and Oktaviani, F. (2022). Aggression in Adolescents: The Role of Mother-Child Attachment and Self-Esteem. Behav. Sci., 12.","DOI":"10.3390\/bs12050147"},{"key":"ref_3","first-page":"1117","article-title":"A review on video violence detection approaches","volume":"13","author":"Shubber","year":"2022","journal-title":"Int. J. Nonlinear Anal. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e20154079","DOI":"10.1542\/peds.2015-4079","article-title":"Global prevalence of past-year violence against children: A systematic review and minimum estimates","volume":"137","author":"Hillis","year":"2016","journal-title":"Pediatrics"},{"key":"ref_5","unstructured":"(2024, February 01). Crime, Safety and Victims\u2019 Rights: Fundamental Rights Survey. Available online: https:\/\/fra.europa.eu\/sites\/default\/files\/fra_uploads\/fra-2021-crime-safety-victims-rights_en.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Gonz\u00e1lez, M.B., Turizo-Palencia, Y., Arenas-Rivera, C., Acu\u00f1a-Rodr\u00edguez, M., G\u00f3mez-L\u00f3pez, Y., and Clemente-Su\u00e1rez, V.J. (2021). Gender, Anxiety, and Legitimation of Violence in Adolescents Facing Simulated Physical Aggression at School. Brain Sci., 11.","DOI":"10.3390\/brainsci11040458"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1186\/s12887-022-03808-y","article-title":"The relationship between smartphone addiction and aggression among Lebanese adolescents: The indirect effect of cognitive function","volume":"22","author":"Malaeb","year":"2022","journal-title":"BMC Pediatr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100163","DOI":"10.1016\/j.jadr.2021.100163","article-title":"Increasing aggression during the COVID-19 lockdowns","volume":"5","author":"Killgore","year":"2021","journal-title":"J. Affect. Disord. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104241","DOI":"10.1016\/j.jpubeco.2020.104241","article-title":"Sheltering in place and domestic violence: Evidence from calls for service during COVID-19","volume":"189","author":"Leslie","year":"2020","journal-title":"J. Public Econ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105217","DOI":"10.1016\/j.worlddev.2020.105217","article-title":"COVID-19 and the rise of intimate partner violence","volume":"137","year":"2021","journal-title":"World Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s12103-020-09551-3","article-title":"Has COVID-19 changed crime? Crime rates in the United States during the pandemic","volume":"45","author":"Boman","year":"2020","journal-title":"Am. J. Crim. Justice"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.dss.2018.07.003","article-title":"Improving crime count forecasts using Twitter and taxi data","volume":"113","author":"Vomfell","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jing, F., Liu, L., Zhou, S., Song, J., Wang, L., Zhou, H., Wang, Y., and Ma, R. (2021). Assessing the impact of street-view greenery on fear of neighborhood crime in Guangzhou, China. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18010311"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yue, H., Xie, H., Liu, L., and Chen, J. (2022). Detecting people on the street and the streetscape physical environment from Baidu street view images and their effects on community-level street crime in a Chinese city. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11030151"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s10940-021-09506-9","article-title":"Measuring the built environment with google street view and machine learning: Consequences for crime on street segments","volume":"38","author":"Hipp","year":"2021","journal-title":"J. Quant. Criminol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shukla, H., and Pandey, M. (2020). Human Suspicious Activity Recognition. Int. Innov. Res. J. Eng. Technol., 5.","DOI":"10.32595\/iirjet.org\/v5i4.2020.130"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cheng, M., Cai, K., and Li, M. (2021, January 10\u201315). RWF-2000: An open large scale video database for violence detection. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412502"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118523","DOI":"10.1016\/j.eswa.2022.118523","article-title":"Multimodal fusion methods with deep neural networks and meta-information for aggression detection in surveillance","volume":"211","author":"Jaafar","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TBIOM.2022.3233399","article-title":"A Multi-Scale Spatio-Temporal Network for Violence Behavior Detection","volume":"5","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e920","DOI":"10.7717\/peerj-cs.920","article-title":"State-of-the-art violence detection techniques in video surveillance security systems: A systematic review","volume":"8","author":"Omarov","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123151","DOI":"10.1016\/j.physa.2019.123151","article-title":"Early warning system: From face recognition by surveillance cameras to social media analysis to detecting suspicious people","volume":"540","author":"Afra","year":"2020","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vosta, S., and Yow, K.C. (2022). A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras. Appl. Sci., 12.","DOI":"10.3390\/app12031021"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alonso, R.S., Sitt\u00f3n-Candanedo, I., Casado-Vara, R., Prieto, J., and Corchado, J.M. (2020). Deep reinforcement learning for the management of software-defined networks and network function virtualization in an edge-IoT architecture. Sustainability, 12.","DOI":"10.3390\/su12145706"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.48161\/qaj.v1n2a46","article-title":"A Comprehensive Survey of Big Data Mining Approaches in Cloud Systems","volume":"1","author":"Ageed","year":"2021","journal-title":"Qubahan Acad. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102383","DOI":"10.1016\/j.ijinfomgt.2021.102383","article-title":"Artificial intelligence in information systems research: A systematic literature review and research agenda","volume":"60","author":"Collins","year":"2021","journal-title":"Int. J. Inf. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/JPROC.2021.3059994","article-title":"Advances in video compression system using deep neural network: A review and case studies","volume":"109","author":"Ding","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kostka, G., Steinacker, L., and Meckel, M. (2024, February 01). Between Privacy and Convenience: Facial Recognition Technology in the Eyes of Citizens in China, Germany, the UK and the US (10 February 2020). Available online: https:\/\/ssrn.com\/abstract=3518857.","DOI":"10.2139\/ssrn.3518857"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ijinfomgt.2018.07.009","article-title":"Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review","volume":"43","author":"Ali","year":"2018","journal-title":"Int. J. Inf. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mugunga, I., Dong, J., Rigall, E., Guo, S., Madessa, A.H., and Nawaz, H.S. (2021, January 23\u201325). A frame-based feature model for violence detection from surveillance cameras using ConvLSTM network. Proceedings of the 2021 6th International Conference on Image, Vision and Computing (ICIVC), Qingdao, China.","DOI":"10.1109\/ICIVC52351.2021.9526948"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Negre, P., Alonso, R.S., Prieto, J., Arrieta, A.G., and Corchado, J.M. (2023). Review of Physical Aggression Detection Techniques in Video Using Explainable Artificial Intelligence. Proceedings of the International Symposium on Ambient Intelligence, Springer.","DOI":"10.1007\/978-3-031-43461-7_6"},{"key":"ref_31","first-page":"29","article-title":"State-of-the-Art Violence Detection Techniques: A review","volume":"13","author":"Siddique","year":"2022","journal-title":"Asian J. Res. Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Negre, P., Alonso, R.S., Prieto, J., Dang, C.N., and Corchado, J.M. (2024, February 01). Systematic Mapping Study on Violence Detection in Video by Means of Trustworthy Artificial Intelligence. Available online: https:\/\/ssrn.com\/abstract=4757631.","DOI":"10.2139\/ssrn.4757631"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/23335777.2021.1940303","article-title":"A survey of video violence detection","volume":"9","author":"Yao","year":"2023","journal-title":"Cyber-Phys. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kaur, G., and Singh, S. (2022). Violence detection in videos using deep learning: A survey. Advances in Information Communication Technology and Computing: Proceedings of AICTC 2021, Springer.","DOI":"10.1007\/978-981-19-0619-0_15"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Talha, K.R., Bandapadya, K., and Khan, M.M. (2022, January 6\u20139). Violence Detection Using Computer Vision Approaches. Proceedings of the 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA.","DOI":"10.1109\/AIIoT54504.2022.9817374"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Madhavan, R., and Vidhya, J. (2021, January 23\u201324). Violence Detection from CCTV Footage Using Optical Flow and Deep Learning in Inconsistent Weather and Lighting Conditions. Proceedings of the Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India. Revised Selected Papers, Part I 5.","DOI":"10.1007\/978-3-030-81462-5_56"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1049\/cvi2.12162","article-title":"Violence 4D: Violence detection in surveillance using 4D convolutional neural networks","volume":"17","author":"Magdy","year":"2023","journal-title":"IET Computer Vision"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, B., and Liu, Y. (2021, January 22\u201324). ESTN: Exacter Spatiotemporal Networks for Violent Action Recognition. Proceedings of the 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), Nanjing, China.","DOI":"10.1109\/ICSIP52628.2021.9688873"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wintarti, A., Puspitasari, R.D.I., and Imah, E.M. (2022, January 10\u201311). Violent Videos Classification Using Wavelet and Support Vector Machine. Proceedings of the 2022 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia.","DOI":"10.1109\/ICISS55894.2022.9915100"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lohithashva, B., and Aradhya, V.M. (2021, January 30\u201331). Violent video event detection: A local optimal oriented pattern based approach. Proceedings of the Applied Intelligence and Informatics: First International Conference, AII 2021, Nottingham, UK. Proceedings 1.","DOI":"10.1007\/978-3-030-82269-9_21"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, L. (2022, January 19\u201321). End-to-end video violence detection with transformer. Proceedings of the 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Chengdu, China.","DOI":"10.1109\/PRAI55851.2022.9904115"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hung, L.P., Yang, C.W., Lee, L.H., and Chen, C.L. (2021). Constructing a Violence Recognition Technique for Elderly Patients with Lower Limb Disability. Proceedings of the International Conference on Smart Grid and Internet of Things, Springer.","DOI":"10.1007\/978-3-031-20398-5_3"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mahalle, M.D., and Rojatkar, D.V. (2021, January 2\u20134). Audio based violent scene detection using extreme learning machine algorithm. Proceedings of the 2021 6th international conference for convergence in technology (I2CT), Maharashtra, India.","DOI":"10.1109\/I2CT51068.2021.9418209"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, W., Ye, L., Fang, L., and Zhang, Q. (2021, January 22\u201324). Violent scene detection of film videos based on multi-task learning of temporal-spatial features. Proceedings of the 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR), Tokyo, Japan.","DOI":"10.1109\/MIPR51284.2021.00067"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Akt\u0131, \u015e., Ofli, F., Imran, M., and Ekenel, H.K. (2022, January 3\u20138). Fight detection from still images in the wild. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACVW54805.2022.00061"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1007\/s00371-023-02865-3","article-title":"An accurate violence detection framework using unsupervised spatial\u2013temporal action translation network","volume":"40","author":"Ehsan","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1109\/TII.2021.3116377","article-title":"AI-assisted edge vision for violence detection in IoT-based industrial surveillance networks","volume":"18","author":"Ullah","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10400","DOI":"10.1002\/int.22537","article-title":"An intelligent system for complex violence pattern analysis and detection","volume":"37","author":"Ullah","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Vijeikis, R., Raudonis, V., and Dervinis, G. (2022). Efficient violence detection in surveillance. Sensors, 22.","DOI":"10.3390\/s22062216"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s42979-020-00207-x","article-title":"CNN-BiLSTM model for violence detection in smart surveillance","volume":"1","author":"Halder","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Traor\u00e9, A., and Akhloufi, M.A. (2020, January 24\u201326). 2D bidirectional gated recurrent unit convolutional neural networks for end-to-end violence detection in videos. Proceedings of the International Conference on Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-030-50347-5_14"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Aarthy, K., and Nithya, A.A. (2022, January 16\u201317). Crowd Violence Detection in Videos Using Deep Learning Architecture. Proceedings of the 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India.","DOI":"10.1109\/MysuruCon55714.2022.9972624"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1007\/s00371-020-01878-6","article-title":"Multi-frame feature-fusion-based model for violence detection","volume":"37","author":"Asad","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_54","unstructured":"Contardo, P., Tomassini, S., Falcionelli, N., Dragoni, A.F., and Sernani, P. (2023, January 26\u201327). Combining a mobile deep neural network and a recurrent layer for violence detection in videos. Proceedings of the RTA-CSIT 2023: 5th International Conference Recent Trends and Applications in Computer Science and Information Technology, Tirana, Albania."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gupta, H., and Ali, S.T. (2022, January 25\u201327). Violence Detection using Deep Learning Techniques. Proceedings of the 2022 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), Hyderabad, India.","DOI":"10.1109\/ICETCI55171.2022.9921388"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Islam, M.S., Hasan, M.M., Abdullah, S., Akbar, J.U.M., Arafat, N., and Murad, S.A. (2021, January 21\u201323). A deep Spatio-temporal network for vision-based sexual harassment detection. Proceedings of the 2021 Emerging Technology in Computing, Communication and Electronics (ETCCE), Dhaka, Bangladesh.","DOI":"10.1109\/ETCCE54784.2021.9689891"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8549","DOI":"10.1007\/s13369-021-05589-5","article-title":"Mobile neural architecture search network and convolutional long short-term memory-based deep features toward detecting violence from video","volume":"46","author":"Jahlan","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mumtaz, N., Ejaz, N., Aladhadh, S., Habib, S., and Lee, M.Y. (2022). Deep multi-scale features fusion for effective violence detection and control charts visualization. Sensors, 22.","DOI":"10.3390\/s22239383"},{"key":"ref_59","first-page":"3374","article-title":"A fully integrated violence detection system using CNN and LSTM","volume":"11","author":"Sharma","year":"2021","journal-title":"Int. J. Electr. Comput. Eng. (2088-8708)"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Singh, N., Prasad, O., and Sujithra, T. (2022, January 25\u201326). Deep Learning-Based Violence Detection from Videos. Proceedings of the Intelligent Data Engineering and Analytics: Proceedings of the 9th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2021), Mizoram, India.","DOI":"10.1007\/978-981-16-6624-7_32"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10515-022-00323-3","article-title":"UAV surveillance for violence detection and individual identification","volume":"29","author":"Srivastava","year":"2022","journal-title":"Autom. Softw. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Islam, Z., Rukonuzzaman, M., Ahmed, R., Kabir, M.H., and Farazi, M. (2021, January 18\u201322). Efficient two-stream network for violence detection using separable convolutional lstm. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Virtual.","DOI":"10.1109\/IJCNN52387.2021.9534280"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"20945","DOI":"10.1007\/s11042-022-12532-9","article-title":"Violence detection in videos using interest frame extraction and 3D convolutional neural network","volume":"81","author":"Mahmoodi","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Ramzan, M., Khan, H.U., Iqbal, S., Khan, M.A., Choi, J.I., Nam, Y., and Kadry, S. (2021). Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning, Tech Science Press.","DOI":"10.32604\/cmc.2021.018103"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2578","DOI":"10.1587\/transinf.2020EDP7056","article-title":"Predicting Violence Rating Based on Pairwise Comparison","volume":"103","author":"Ji","year":"2020","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ehsan, T.Z., and Mohtavipour, S.M. (2020, January 22\u201323). Vi-Net: A deep violent flow network for violence detection in video sequences. Proceedings of the 2020 11th International Conference on Information and Knowledge Technology (IKT), Tehran, Iran.","DOI":"10.1109\/IKT51791.2020.9345617"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jayasimhan, A., and Pabitha, P. (2022, January 15\u201316). A hybrid model using 2D and 3D Convolutional Neural Networks for violence detection in a video dataset. Proceedings of the 2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4), Bangalore, India.","DOI":"10.1109\/C2I456876.2022.10051324"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kim, H., Jeon, H., Kim, D., and Kim, J. (2022, January 19\u201321). Lightweight framework for the violence and falling-down event occurrence detection for surveillance videos. Proceedings of the 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICTC55196.2022.9952688"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Monteiro, C., and Dur\u00e3es, D. (2022, January 16\u201319). Modelling a Framework to Obtain Violence Detection with Spatial-Temporal Action Localization. Proceedings of the World Conference on Information Systems and Technologies, Galicia, Spain.","DOI":"10.1007\/978-3-031-04826-5_62"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yuan, D., Li, X., and Su, S. (2022, January 15\u201320). Violent Target Detection Based on Improved YOLO Network. Proceedings of the International Conference on Artificial Intelligence and Security, Qinghai, China.","DOI":"10.1007\/978-3-031-06767-9_40"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Appavu, N. (May, January 29). Violence Detection Based on Multisource Deep CNN with Handcraft Features. Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Hammamet, Tunisia.","DOI":"10.1109\/IC_ASET58101.2023.10150949"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Adithya, H., Lekhashree, H., and Raghuram, S. (2023, January 26\u201330). Violence Detection in Drone Surveillance Videos. Proceedings of the International Conference on Smart Computing and Communication, Nashville, TN, USA.","DOI":"10.1007\/978-981-99-0838-7_60"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Bi, Y., Li, D., and Luo, Y. (2022). Combining keyframes and image classification for violent behavior recognition. Appl. Sci., 12.","DOI":"10.3390\/app12168014"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s00138-021-01264-9","article-title":"Inflated 3D ConvNet context analysis for violence detection","volume":"33","author":"Barra","year":"2022","journal-title":"Mach. Vis. Appl."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Gkountakos, K., Ioannidis, K., Tsikrika, T., Vrochidis, S., and Kompatsiaris, I. (2020, January 8\u201311). A crowd analysis framework for detecting violence scenes. Proceedings of the 2020 International Conference on Multimedia Retrieval, Dublin, Ireland.","DOI":"10.1145\/3372278.3390725"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"18772","DOI":"10.1109\/ACCESS.2023.3245521","article-title":"Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications","volume":"11","author":"Adhikarla","year":"2023","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Jain, A., and Vishwakarma, D.K. (2020, January 20\u201322). Deep NeuralNet for violence detection using motion features from dynamic images. Proceedings of the 2020 third international conference on smart systems and inventive technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214153"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Liang, Q., Cheng, C., Li, Y., Yang, K., and Chen, B. (2021, January 23\u201325). Fusion and visualization design of violence detection and geographic video. Proceedings of the Theoretical Computer Science: 39th National Conference of Theoretical Computer Science, NCTCS 2021, Yinchuan, China. Revised Selected Papers 39.","DOI":"10.1007\/978-981-16-7443-3_3"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1093\/comjnl\/bxaa061","article-title":"Fast learning through deep multi-net CNN model for violence recognition in video surveillance","volume":"65","author":"Mumtaz","year":"2022","journal-title":"Comput. J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"19545","DOI":"10.1007\/s11227-022-04649-3","article-title":"A time sequence location method of long video violence based on improved C3D network","volume":"78","author":"Qu","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Santos, F., Dur\u00e3es, D., Marcondes, F.S., Lange, S., Machado, J., and Novais, P. (2021, January 6\u20138). Efficient violence detection using transfer learning. Proceedings of the International Conference on Practical Applications of Agents and Multi-Agent Systems, Salamanca, Spain.","DOI":"10.1007\/978-3-030-85710-3_6"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"160580","DOI":"10.1109\/ACCESS.2021.3131315","article-title":"Deep learning for automatic violence detection: Tests on the AIRTLab dataset","volume":"9","author":"Sernani","year":"2021","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Shang, Y., Wu, X., and Liu, R. (2022, January 4\u20137). Multimodal Violent Video Recognition Based on Mutual Distillation. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Shenzhen, China.","DOI":"10.1007\/978-3-031-18913-5_48"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11704-019-8266-2","article-title":"Multipath affinage stacked\u2014Hourglass networks for human pose estimation","volume":"14","author":"Hua","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1109\/TCSVT.2018.2858828","article-title":"Human pose estimation in video via structured space learning and halfway temporal evaluation","volume":"29","author":"Liu","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1007\/s00371-021-02266-4","article-title":"A multi-stream CNN for deep violence detection in video sequences using handcrafted features","volume":"38","author":"Mohtavipour","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.21817\/indjcse\/2021\/v12i6\/211206165","article-title":"Classification Of Violent Videos Using Ensemble Boosting Machine Learning Approach With Low Level Features","volume":"12","author":"Jaiswal","year":"2021","journal-title":"Indian J. Comput. Sci. Eng."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.ins.2022.05.045","article-title":"TOP-ALCM: A novel video analysis method for violence detection in crowded scenes","volume":"606","author":"Hu","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"18365","DOI":"10.1007\/s11042-021-10682-w","article-title":"Deep-violence: Individual person violent activity detection in video","volume":"80","author":"Naik","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Narynov, S., Zhumanov, Z., Gumar, A., Khassanova, M., and Omarov, B. (October, January 29). Detecting School Violence Using Artificial Intelligence to Interpret Surveillance Video Sequences. Proceedings of the Advances in Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Kallithea, Rhodes, Greece. Proceedings 13.","DOI":"10.1007\/978-3-030-88113-9_32"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1007\/s11554-021-01171-2","article-title":"Recognizing human violent action using drone surveillance within real-time proximity","volume":"18","author":"Srivastava","year":"2021","journal-title":"J. Real-Time Image Process."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Su, Y., Lin, G., Zhu, J., and Wu, Q. (2020, January 23\u201328). Human interaction learning on 3d skeleton point clouds for video violence recognition. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part IV 16.","DOI":"10.1007\/978-3-030-58548-8_5"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., and Yang, Z. (2020, January 23\u201328). Not only look, but also listen: Learning multimodal violence detection under weak supervision. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XXX 16.","DOI":"10.1007\/978-3-030-58577-8_20"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"6435","DOI":"10.1007\/s11227-020-03514-5","article-title":"Video reasoning for conflict events through feature extraction","volume":"77","author":"Cheng","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kumar, A., Shetty, A., Sagar, A., Charushree, A., and Kanwal, P. (2023, January 26\u201328). Indoor Violence Detection using Lightweight Transformer Model. Proceedings of the 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India.","DOI":"10.1109\/INCET57972.2023.10170251"},{"key":"ref_96","unstructured":"Bermejo Nievas, E., Deniz Suarez, O., Bueno Garc\u00eda, G., and Sukthankar, R. (2011, January 29\u201331). Violence detection in video using computer vision techniques. Proceedings of the Computer Analysis of Images and Patterns: 14th International Conference, CAIP 2011, Seville, Spain. Proceedings, Part II 14."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Hassner, T., Itcher, Y., and Kliper-Gross, O. (2012, January 16\u201321). Violent flows: Real-time detection of violent crowd behavior. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Soliman, M.M., Kamal, M.H., El-Massih Nashed, M.A., Mostafa, Y.M., Chawky, B.S., and Khattab, D. (2019, January 8\u201310). Violence Recognition from Videos using Deep Learning Techniques. Proceedings of the 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., and Shah, M. (2018, January 18\u201322). Real-world anomaly detection in surveillance videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref_100","first-page":"4","article-title":"The BEHAVE video dataset: Ground truthed video for multi-person behavior classification","volume":"4","author":"Blunsden","year":"2010","journal-title":"Ann. BMVA"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Akt\u0131, \u015e., Tataro\u011flu, G.A., and Ekenel, H.K. (2019, January 6\u20139). Vision-based fight detection from surveillance cameras. Proceedings of the 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), Istanbul, Turkey.","DOI":"10.1109\/IPTA.2019.8936070"},{"key":"ref_102","unstructured":"Sj\u00f6berg, M., Baveye, Y., Wang, H., Quang, V.L., Ionescu, B., Dellandr\u00e9a, E., Schedl, M., Demarty, C.H., and Chen, L. (2015, January 14\u201315). The MediaEval 2015 Affective Impact of Movies Task. Proceedings of the MediaEval, Wurzen, Germany."},{"key":"ref_103","unstructured":"Li, A., Thotakuri, M., Ross, D.A., Carreira, J., Vostrikov, A., and Zisserman, A. (2020). The AVA-Kinetics Localized Human Actions Video Dataset. arXiv."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"7379","DOI":"10.1007\/s11042-014-1984-4","article-title":"VSD, a public dataset for the detection of violent scenes in movies: Design, annotation, analysis and evaluation","volume":"74","author":"Demarty","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_105","unstructured":"Rachna, U., Guruprasad, V., Shindhe, S.D., and Omkar, S. Real-Time Violence Detection Using Deep Neural Networks and DTW. Proceedings of the International Conference on Computer Vision and Image Processing."},{"key":"ref_106","unstructured":"(2019). Ethics Guidelines for Trustworthy AI, European Commission."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/4016\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:01:48Z","timestamp":1760108508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/4016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,20]]},"references-count":106,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24124016"],"URL":"https:\/\/doi.org\/10.3390\/s24124016","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,20]]}}}