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suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features

Computer Systems Science & Engineering 2023年第11期47卷 2337-2360页

作者： Safdar Khan Muhammad Attique Khan Jamal Hussain Shah Faheem Shehzad Taerang Kim Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Human suspicious activity Recognition(HSAR)is a critical and active research area in computer vision that relies on artificial intelligence *** advances have been made in this field recently due to important applications such as video *** video surveillance,humans are monitored through video cameras when doing suspicious activities such as kidnapping,fighting,snatching,and a few *** numerous techniques have been introduced in the literature for routine human actions(HAR),very few studies are available for *** study proposes a deep convolutional neural network(CNN)and optimal featuresbased framework for HSAR in video *** framework consists of various stages,including preprocessing video frames,fine-tuning deep models(Darknet 19 and Nasnet mobile)using transfer learning,serial-based feature fusion,feature selection via equilibrium feature optimizer,and neural network classifiers for ***-tuning two models using some hit and trial methods is the first challenge of this work that was later employed for feature ***,features are fused in a serial approach,and then an improved optimization method is proposed to select the best *** proposed technique was evaluated on two action datasets,Hybrid-KTH01 and HybridKTH02,and achieved an accuracy of 99.8%and 99.7%,*** proposed method exhibited higher precision compared to existing state-ofthe-art approaches.

关键词： suspicious activity deep learning weights surveillance optimization

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