Smart City Gnosys

Smart city article details

Title Toward Fast And Accurate Violence Detection For Automated Video Surveillance Applications
ID_Doc 57685
Authors Huszar V.D.; Adhikarla V.K.; Negyesi I.; Krasznay C.
Year 2023
Published IEEE Access, 11
DOI http://dx.doi.org/10.1109/ACCESS.2023.3245521
Abstract Surveillance cameras are increasingly being used worldwide due to the proliferation of digital video capturing, storage, and processing technologies. However, the large volume of video data generated makes it difficult for humans to perform real-time analysis, and even manual approaches can result in delayed detection of events. Automatic violence detection in surveillance footage has therefore gained significant attention in the scientific community as a way to address this challenge. With the advancement of machine learning algorithms, automatic video recognition tasks such as violence detection have become increasingly feasible. In this study, we investigate the use of smart networks that model the dynamic relationships between actors and/or objects using 3D convolutions to capture both the spatial and temporal structure of the data. We also leverage the knowledge learned by a pre-trained action recognition model for efficient and accurate violence detection in surveillance footage. We extend and evaluate several public datasets featuring diverse and challenging video content to assess the effectiveness of our proposed methods. Our results show that our approach outperforms state-of-the-art methods, achieving approximately a 2% improvement in accuracy with fewer model parameters. Additionally, our experiments demonstrate the robustness of our approach under common compression artifacts encountered in remote server processing applications. © 2013 IEEE.
Author Keywords Anomaly detection; anomaly localization; automated video surveillance; deep learning; efficient violence detection; human activity recognition; security; smart cities; video recognition; violence detection


Similar Articles

Id Similarity Authors Title Published
7918 View0.918Khan M.; Gueaieb W.; Saddik A.E.; De Masi G.; Karray F.An Efficient Violence Detection Approach For Smart Cities Surveillance SystemProceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023 (2023)
61134 View0.911Khan H.; Yuan X.; Qingge L.; Roy K.Violence Detection From Industrial Surveillance Videos Using Deep LearningIEEE Access, 13 (2025)
60873 View0.908Khan M.; Saddik A.E.; Gueaieb W.; De Masi G.; Karray F.Vd-Net: An Edge Vision-Based Surveillance System For Violence DetectionIEEE Access, 12 (2024)
3466 View0.905Elzein A.; Basaran E.; Yang Y.D.; Qaraqe M.A Novel Multi-Scale Violence And Public Gathering Dataset For Crowd Behavior ClassificationFrontiers in Computer Science, 6 (2024)
950 View0.903Ullah F.U.M.; Obaidat M.S.; Ullah A.; Muhammad K.; Hijji M.; Baik S.W.A Comprehensive Review On Vision-Based Violence Detection In Surveillance VideosACM Computing Surveys, 55, 10 (2023)
34805 View0.901Azzakhnini M.; Saidi H.; Azough A.; Tairi H.; Qjidaa H.Lavid: A Lightweight And Autonomous Smart Camera System For Urban Violence Detection And GeolocationComputers, 14, 4 (2025)
8959 View0.898Mumtaz 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 DirectionsArtificial Intelligence Review, 56, 5 (2023)
22444 View0.896Ren X.; Fan W.; Wang Y.Efficiently Adapting Large Pre-Trained Models For Real-Time Violence Recognition In Smart City SurveillanceJournal of Real-Time Image Processing, 21, 4 (2024)
46921 View0.887Abdali A.-M.R.; Al-Tuma R.F.Robust Real-Time Violence Detection In Video Using Cnn And LstmSCCS 2019 - 2019 2nd Scientific Conference of Computer Sciences (2019)
18135 View0.883Al-Mamun Provath M.; Rahman M.; Deb K.; Kumar Dhar P.; Shimamura T.Deepguard: Enhancing Violence Detection In Smart Cities Through Deep LearningIEEE Access, 13 (2025)