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Title	Ai-Assisted Edge Vision For Violence Detection In Iot-Based Industrial Surveillance Networks
ID_Doc	6978
Authors	Ullah F.U.M.; Muhammad K.; Haq I.U.; Khan N.; Heidari A.A.; Baik S.W.; De Albuquerque V.H.C.
Year	2022
Published	IEEE Transactions on Industrial Informatics, 18, 8
DOI	http://dx.doi.org/10.1109/TII.2021.3116377
Abstract	Analyzing surveillance videos is mandatory for the public and industrial security. Overwhelming growth in computer vision fields has been made to automate the surveillance system in terms of human activity recognition, such as behavior analysis and violence detection (VD). However, it is challenging to detect and analyze the violent scenes intelligently to fulfill the notion of Industrial Internet of Things (IIoT)-based surveillance buoyed by constrained resources to reduce computational power. To tackle this challenge, in this article, an artificial intelligence enabled IIoT-based framework with VD-Network (VD-Net) is proposed. First, the input video frames are passed to light-weight convolutional neural network model for important information collection including humans or suspicious objects such as knives/guns. Upon suspicious object detection, an alert is generated as an earlier VD in IIoT network while the information is shared with concern departments. Only the frames with objects are forwarded to cloud for detail investigation where features are extracted using convolutional long short-term memory (ConvLSTM). The latter from ConvLSTM is propagated to gated recurrent unit for final VD. The conducted experiments and ablation study on the existing surveillance and nonsurveillance datasets empirically validate the effectiveness of the proposed VD-Net by improving 3.9% increase in the accuracy compared with the state-of-the-art VD methods. © 2005-2012 IEEE.
Author Keywords	Artificial IoT; cloud computing; collaborative industrial Internet of Thing (IIoT); deep learning; edge intelligence; industrial security; smart city; violence detection

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