Smart City Gnosys

Smart city article details

Title	A Comprehensive Review On Vision-Based Violence Detection In Surveillance Videos
ID_Doc	950
Authors	Ullah F.U.M.; Obaidat M.S.; Ullah A.; Muhammad K.; Hijji M.; Baik S.W.
Year	2023
Published	ACM Computing Surveys, 55, 10
DOI	http://dx.doi.org/10.1145/3561971
Abstract	Recent advancements in intelligent surveillance systems for video analysis have been a topic of great interest in the research community due to the vast number of applications to monitor humans' activities. The growing demand for these systems aims towards automatic violence detection (VD) systems enhancing and comforting human lives through artificial neural networks (ANN) and machine intelligence. Extremely overcrowded regions such as subways, public streets, banks, and the industries need such automatic VD system to ensure safety and security in the smart city. For this purpose, researchers have published extensive VD literature in the form of surveys, proposals, and extensive reviews. Existing VD surveys are limited to a single domain of study, i.e., coverage of VD for non-surveillance or for person-To-person data only. To deeply examine and contribute to the VD arena, we survey and analyze the VD literature into a single platform that highlights the working flow of VD in terms of machine learning strategies, neural networks (NNs)-based patterns analysis, limitations in existing VD articles, and their source details. Further, we investigate VD in terms of surveillance datasets and VD applications and debate on the challenges faced by researchers using these datasets. We comprehensively discuss the evaluation strategies and metrics for VD methods. Finally, we emphasize the recommendations in future research guidelines of VD that aid this arena with respect to trending research endeavors. © 2023 Association for Computing Machinery.
Author Keywords	activity recognition; Artificial Intelligence; big data; deep learning; machine learning; neural networks; smart surveillance; video data; violence detection

Similar Articles

Id	Similarity	Authors	Title	Published
8959	0.937	Mumtaz N.; Ejaz N.; Habib S.; Mohsin S.M.; Tiwari P.; Band S.S.; Kumar N.	An Overview Of Violence Detection Techniques: Current Challenges And Future Directions	Artificial Intelligence Review, 56, 5 (2023)
60873	0.935	Khan M.; Saddik A.E.; Gueaieb W.; De Masi G.; Karray F.	Vd-Net: An Edge Vision-Based Surveillance System For Violence Detection	IEEE Access, 12 (2024)
61134	0.909	Khan H.; Yuan X.; Qingge L.; Roy K.	Violence Detection From Industrial Surveillance Videos Using Deep Learning	IEEE Access, 13 (2025)
3466	0.905	Elzein A.; Basaran E.; Yang Y.D.; Qaraqe M.	A Novel Multi-Scale Violence And Public Gathering Dataset For Crowd Behavior Classification	Frontiers in Computer Science, 6 (2024)
57685	0.903	Huszar V.D.; Adhikarla V.K.; Negyesi I.; Krasznay C.	Toward Fast And Accurate Violence Detection For Automated Video Surveillance Applications	IEEE Access, 11 (2023)
34805	0.901	Azzakhnini M.; Saidi H.; Azough A.; Tairi H.; Qjidaa H.	Lavid: A Lightweight And Autonomous Smart Camera System For Urban Violence Detection And Geolocation	Computers, 14, 4 (2025)
6978	0.899	Ullah F.U.M.; Muhammad K.; Haq I.U.; Khan N.; Heidari A.A.; Baik S.W.; De Albuquerque V.H.C.	Ai-Assisted Edge Vision For Violence Detection In Iot-Based Industrial Surveillance Networks	IEEE Transactions on Industrial Informatics, 18, 8 (2022)
17972	0.898	Yadav R.; Gupta A.; Chauhan J.; Rawat R.	Deep Learning-Based Smart Surveillance System	Lecture Notes in Networks and Systems, 1005 LNNS (2024)
22444	0.885	Ren X.; Fan W.; Wang Y.	Efficiently Adapting Large Pre-Trained Models For Real-Time Violence Recognition In Smart City Surveillance	Journal of Real-Time Image Processing, 21, 4 (2024)
18135	0.883	Al-Mamun Provath M.; Rahman M.; Deb K.; Kumar Dhar P.; Shimamura T.	Deepguard: Enhancing Violence Detection In Smart Cities Through Deep Learning	IEEE Access, 13 (2025)