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DL-har: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

Computers, Materials & Continua 2020年第11期65卷 1033-1057页

作者： Abdu Gumaei Mabrook Al-Rakhami hussain AlSalman Sk.Md.Mizanur Rahman Atif Alamri Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11543Saudi Arabia Information and Communication Engineering Technology School of Engineering Technology and Applied ScienceCentennial CollegeTorontoCanada

Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to *** learning has gained momentum for identifying activities through sensors,smartphones or even surveillance ***,it is often difficult to train deep learning models on constrained IoT *** focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing,which we call *** goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on less-powerful edge devices for *** idea is to conduct the training of the model in the Cloud and distribute it to the edge *** demonstrate how the DL-har can perform human activity recognition at the edge while improving efficiency and *** order to evaluate the proposed framework,we conducted a comprehensive set of experiments to validate the applicability of *** results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.

关键词： Human activity recognition edge computing deep neural network recurrent neural network Docker

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Improved Shark Smell Optimization Algorithm for Human Action Recognition

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Computers, Materials & Continua 2023年第9期76卷 2667-2684页

作者： Inzamam Mashood Nasir Mudassar Raza Jamal hussain Shah Muhammad Attique Khan Yun-Cheol Nam Yunyoung Nam Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Architecture Joongbu UniversityGoyang10279South Korea Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Human Action Recognition(har)in uncontrolled environments targets to recognition of different actions froma *** effective har model can be employed for an application like human-computer interaction,health care,person tracking,and video *** Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature *** accuracy and effectiveness of these models continue to be the biggest challenge in this *** this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted *** proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search *** proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of *** local optimal features are selected,a global search is conducted to further optimize these *** proposed iSSO algorithm is employed on nine(9)selected CNN *** CNN models are selected based on their top-1 and top-5 accuracy in ImageNet *** evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected.

关键词： Action recognition improved shark smell optimization convolutional neural networks machine learning

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

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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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