Smart City Gnosys

Smart city article details

Title Enhancing Smart City Safety And Utilizing Ai Expert Systems For Violence Detection
ID_Doc 23964
Authors Kumar P.; Shih G.-L.; Guo B.-L.; Nagi S.K.; Manie Y.C.; Yao C.-K.; Arockiyadoss M.A.; Peng P.-C.
Year 2024
Published Future Internet, 16, 2
DOI http://dx.doi.org/10.3390/fi16020050
Abstract Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system. © 2024 by the authors.
Author Keywords artificial intelligence; edge computing; expert system; image-to-image stable diffusion; LSTM; MediaPipe; real-time application; smart city; violence detection; YOLO v7


Similar Articles


Id Similarity Authors Title Published
30916 View0.892Dalal S.; Lilhore U.K.; Sharma N.; Arora S.; Simaiya S.; Ayadi M.; Almujally N.A.; Ksibi A.Improving Smart Home Surveillance Through Yolo Model With Transfer Learning And Quantization For Enhanced Accuracy And EfficiencyPeerJ Computer Science, 10 (2024)
34805 View0.881Azzakhnini 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)
61134 View0.878Khan H.; Yuan X.; Qingge L.; Roy K.Violence Detection From Industrial Surveillance Videos Using Deep LearningIEEE Access, 13 (2025)
22444 View0.875Ren 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)
44618 View0.872Yahuarcani I.O.; Garcia DIaz J.E.; Nunez Satalaya A.M.; Dominguez Noriega A.A.; Lozano Cachique F.X.; Saravia Llaja L.A.; Pezo A.R.; Lopez Rojas A.E.Recognition Of Violent Actions On Streets In Urban Spaces Using Machine Learning In The Context Of The Covid-19 PandemicInternational Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 (2021)
60873 View0.871Khan 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)
950 View0.87Ullah 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)
4582 View0.868Baba, M; Gui, V; Cernazanu, C; Pescaru, DA Sensor Network Approach For Violence Detection In Smart Cities Using Deep LearningSENSORS, 19, 7 (2019)
1374 View0.868Chatterjee R.; Chatterjee A.; Pradhan M.R.; Acharya B.; Choudhury T.A Deep Learning-Based Efficient Firearms Monitoring Technique For Building Secure Smart CitiesIEEE Access, 11 (2023)
57685 View0.868Huszar V.D.; Adhikarla V.K.; Negyesi I.; Krasznay C.Toward Fast And Accurate Violence Detection For Automated Video Surveillance ApplicationsIEEE Access, 11 (2023)