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221110 ||| eng |
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|a 9783036551999
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|a 9783036552002
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|a books978-3-0365-5200-2
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| 100 |
1 |
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|a Lee, Hyo Jong
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| 245 |
0 |
0 |
|a Deep Learning-Based Action Recognition
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (240 p.)
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| 653 |
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|a spatio-temporal differential
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| 653 |
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|a human action recognition
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| 653 |
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|a spatio-temporal feature
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| 653 |
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|a partitioned centerpose network
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| 653 |
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|a multi-modalities network
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| 653 |
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|a high-order feature
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| 653 |
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|a feature fusion
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| 653 |
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|a n/a
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| 653 |
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|a data augmentation
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| 653 |
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|a stacked hourglass network
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| 653 |
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|a gesture classification
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| 653 |
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|a Dynamic Hand Gesture Recognition
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| 653 |
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|a deep learning
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| 653 |
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|a human-computer interaction
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| 653 |
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|a human-machine interface
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a spatiotemporal feature
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| 653 |
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|a feedforward neural networks
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| 653 |
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|a class-specific features
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a multi-person pose estimation
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| 653 |
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|a graph convolution
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| 653 |
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|a transfer learning
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| 653 |
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|a fusion strategies
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| 653 |
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|a real-time
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| 653 |
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|a pose estimation
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| 653 |
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|a dynamic gesture recognition
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| 653 |
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|a convolutional receptive field
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| 653 |
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|a continuous hand gesture recognition
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| 653 |
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|a hand gesture recognition
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| 653 |
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|a partition pose representation
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| 653 |
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|a Long Short-Term Memory
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| 653 |
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|a multi-modal features
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| 653 |
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|a CNN
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| 653 |
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|a action recognition
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| 653 |
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|a class regularization
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| 653 |
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|a hand shape features
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| 653 |
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|a 3D-CNN
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| 653 |
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|a artificial intelligence
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| 653 |
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|a spatio-temporal image formation
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| 653 |
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|a gesture spotting
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| 653 |
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|a 3D skeletal
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| 653 |
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|a embedded system
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| 653 |
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|a spatiotemporal activations
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| 653 |
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|a activity recognition
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| 653 |
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|a human activity recognition
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| 700 |
1 |
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|a Lee, Hyo Jong
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-5200-2
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| 856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/6107
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/93210
|z DOAB: description of the publication
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| 082 |
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|a 900
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|a 140
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|a 700
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|a 600
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|a 620
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|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.
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