Smart City Gnosys

Smart city article details

Title	A Sensor Network Approach For Violence Detection In Smart Cities Using Deep Learning
ID_Doc	4582
Authors	Baba, M; Gui, V; Cernazanu, C; Pescaru, D
Year	2019
Published	SENSORS, 19, 7
DOI	http://dx.doi.org/10.3390/s19071676
Abstract	Citizen safety in modern urban environments is an important aspect of life quality. Implementation of a smart city approach to video surveillance depends heavily on the capability of gathering and processing huge amounts of live urban data. Analyzing data from high bandwidth surveillance video streams provided by large size distributed sensor networks is particularly challenging. We propose here an efficient method for automatic violent behavior detection designed for video sensor networks. Known solutions to real-time violence detection are not suitable for implementation in a resource-constrained environment due to the high processing power requirements. Our algorithm achieves real-time processing on a Raspberry PI-embedded architecture. To ensure separation of temporal and spatial information processing we employ a computationally effective cascaded approach. It consists of a deep neural network followed by a time domain classifier. In contrast with current approaches, the deep neural network input is fed exclusively with motion vector features extracted directly from the MPEG encoded video stream. As proven by results, we achieve state-of-the-art performance, while running on a low computational resources embedded architecture.
Author Keywords	sensor networks; deep learning; action classification; violence detection; smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
61134	0.907	Khan H.; Yuan X.; Qingge L.; Roy K.	Violence Detection From Industrial Surveillance Videos Using Deep Learning	IEEE Access, 13 (2025)
34805	0.903	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)
7918	0.895	Khan M.; Gueaieb W.; Saddik A.E.; De Masi G.; Karray F.	An Efficient Violence Detection Approach For Smart Cities Surveillance System	Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023 (2023)
3466	0.889	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)
18135	0.888	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)
60873	0.887	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)
950	0.882	Ullah 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 Videos	ACM Computing Surveys, 55, 10 (2023)
8959	0.881	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)
1323	0.879	Song Z.; Zhang W.; Chen D.	A Deep Fusion Network For Violence Recognition	Proceedings - 2022 4th International Conference on Intelligent Information Processing, IIP 2022 (2022)
22444	0.878	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)