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Title	Vd-Net: An Edge Vision-Based Surveillance System For Violence Detection
ID_Doc	60873
Authors	Khan M.; Saddik A.E.; Gueaieb W.; De Masi G.; Karray F.
Year	2024
Published	IEEE Access, 12
DOI	http://dx.doi.org/10.1109/ACCESS.2024.3380192
Abstract	The automation of surveillance systems, driven by the rapid development of computer vision technology, has significantly enhanced the analysis of surveillance videos, particularly in recognition of human activity, including behavior analysis and violence detection, thereby bolstering public and industrial security. Despite these advancements, detecting and analyzing violent actions remains challenging, especially for real-time surveillance systems with limited computing power. We propose an artificial intelligence-based framework called VD-Net (Violence Detection Network), enabled by Intelligent Internet-of-Things (IIoT) to detect violent behavior in public and private spaces. The model utilizes lightweight special task temporal convolutional network (ST-TCN) blocks and several bottleneck layers to focus on salient features in the input sequence. The learned features passed from the classifier to discriminate between violent and nonviolent actions. Additionally, our system is supposed to trigger an alert if violence is detected, which is then communicated to relevant departments. We checked the robustness of our system by surveillance and non-surveillance datasets and ensured a 1-4 % improvement in State-of-The-Art (SoTA) accuracy. © 2013 IEEE.
Author Keywords	Artificial intelligence; cloud computing; edge intelligence; Internet of Things (IoT); security; smart city; violence detection

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