Deep Learning-Based Action Recognition

The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the proce...

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Bibliographic Details
Other Authors: Lee, Hyo Jong (Editor)
Format: Electronic Book Chapter
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:DOAB: download the publication
DOAB: description of the publication
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245 1 0 |a Deep Learning-Based Action Recognition 
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300 |a 1 electronic resource (240 p.) 
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520 |a The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values ​​of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a Technology: general issues  |2 bicssc 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a human action recognition 
653 |a graph convolution 
653 |a high-order feature 
653 |a spatio-temporal feature 
653 |a feature fusion 
653 |a dynamic gesture recognition 
653 |a multi-modalities network 
653 |a class regularization 
653 |a 3D-CNN 
653 |a spatiotemporal activations 
653 |a class-specific features 
653 |a Dynamic Hand Gesture Recognition 
653 |a human-computer interaction 
653 |a hand shape features 
653 |a pose estimation 
653 |a stacked hourglass network 
653 |a deep learning 
653 |a convolutional receptive field 
653 |a hand gesture recognition 
653 |a human-machine interface 
653 |a artificial intelligence 
653 |a feedforward neural networks 
653 |a spatio-temporal image formation 
653 |a human activity recognition 
653 |a fusion strategies 
653 |a transfer learning 
653 |a activity recognition 
653 |a data augmentation 
653 |a multi-person pose estimation 
653 |a partitioned centerpose network 
653 |a partition pose representation 
653 |a continuous hand gesture recognition 
653 |a gesture spotting 
653 |a gesture classification 
653 |a multi-modal features 
653 |a 3D skeletal 
653 |a CNN 
653 |a spatiotemporal feature 
653 |a embedded system 
653 |a real-time 
653 |a action recognition 
653 |a Long Short-Term Memory 
653 |a spatio-temporal differential 
653 |a n/a 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/93210  |7 0  |z DOAB: description of the publication