Journal ArticleDOI
Sensor-based and vision-based human activity recognition: A comprehensive survey
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This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category.About:
This article is published in Pattern Recognition.The article was published on 2020-12-01. It has received 263 citations till now.read more
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Journal ArticleDOI
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges
Aleksandr Ometov,Viktoriia Shubina,Lucie Klus,Justyna Skibinska,Salwa Saafi,Pavel Pascacio,Laura Flueratoru,Darwin Quezada Gaibor,Nadezhda Chukhno,Olga Chukhno,Asad Ali,Asma Channa,Ekaterina Svertoka,Ekaterina Svertoka,Waleed Bin Qaim,Raúl Casanova-Marqués,Raúl Casanova-Marqués,Sylvia Holcer,Sylvia Holcer,Joaquín Torres-Sospedra,Sven Casteleyn,Giuseppe Ruggeri,Giuseppe Araniti,Radim Burget,Jiri Hosek,Elena Simona Lohan +25 more
TL;DR: An extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology are provided.
Journal ArticleDOI
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes
TL;DR: In this article, the authors proposed a generic HAR framework for smartphone sensor data, based on Long Short-Term Memory (LSTM) networks for time-series domains, and a hybrid LSTM network was proposed to improve recognition performance.
Journal ArticleDOI
A federated learning system with enhanced feature extraction for human activity recognition
TL;DR: Experimental results demonstrate that PEN outperforms 14 existing HAR algorithms on these datasets in terms of the F1-score; HARFLS with PEN obtains better recognition results on the WISDM and PAMAP2 datasets, compared with 11 existing federated learning systems with various feature extraction structures.
Journal ArticleDOI
Human Fall Detection in Surveillance Videos Using Fall Motion Vector Modeling
TL;DR: Using fall motion vector, this work is able to efficiently identify fall events in varieties of scenarios, such as the narrow angle camera (Le2i dataset), wide angles camera (URFall dataset), and multiple cameras (Montreal dataset).
Journal ArticleDOI
A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving
Yanfen Li,Hanxiang Wang,L. Minh Dang,Tan N. Nguyen,Dongil Han,Ahyun Lee,Insung Jang,Hyeonjoon Moon +7 more
TL;DR: A vision-based system was developed to detect and identity various objects and predict the intention of pedestrians in the traffic scene and results proved that the total parameters of optimized YOLOv4 are reduced by 74%, which satisfies the real-time capability.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book
Finite Mixture Models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the mathematical and statistical literature.
Proceedings ArticleDOI
Large-Scale Video Classification with Convolutional Neural Networks
TL;DR: This work studies multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggests a multiresolution, foveated architecture as a promising way of speeding up the training.
Posted Content
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
TL;DR: This work introduces UCF101 which is currently the largest dataset of human actions and provides baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%.