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...
-д хадгалсан:
| Формат: | Online |
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| Хэл сонгох: | англи |
| Хэвлэсэн: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Нөхцлүүд: | |
| Онлайн хандалт: | ONIX_20221025_9783036551999_64 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1869515936035241984 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-93210 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-932102024-04-09T23:15:56Z Deep Learning-Based Action Recognition Lee, Hyo Jong human action recognition graph convolution high-order feature spatio-temporal feature feature fusion dynamic gesture recognition multi-modalities network class regularization 3D-CNN spatiotemporal activations class-specific features Dynamic Hand Gesture Recognition human-computer interaction hand shape features pose estimation stacked hourglass network deep learning convolutional receptive field hand gesture recognition human–machine interface artificial intelligence feedforward neural networks spatio-temporal image formation human activity recognition fusion strategies transfer learning activity recognition data augmentation multi-person pose estimation partitioned centerpose network partition pose representation continuous hand gesture recognition gesture spotting gesture classification multi-modal features 3D skeletal CNN spatiotemporal feature embedded system real-time action recognition Long Short-Term Memory spatio–temporal differential n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2022-10-25T09:02:19Z 2022-10-25T09:02:19Z 2022 book ONIX_20221025_9783036551999_64 9783036551999 9783036552002 https://directory.doabooks.org/handle/20.500.12854/93210 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/6107 https://mdpi.com/books/pdfview/book/6107 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5200-2 10.3390/books978-3-0365-5200-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036551999 9783036552002 240 open access |
| spellingShingle | human action recognition graph convolution high-order feature spatio-temporal feature feature fusion dynamic gesture recognition multi-modalities network class regularization 3D-CNN spatiotemporal activations class-specific features Dynamic Hand Gesture Recognition human-computer interaction hand shape features pose estimation stacked hourglass network deep learning convolutional receptive field hand gesture recognition human–machine interface artificial intelligence feedforward neural networks spatio-temporal image formation human activity recognition fusion strategies transfer learning activity recognition data augmentation multi-person pose estimation partitioned centerpose network partition pose representation continuous hand gesture recognition gesture spotting gesture classification multi-modal features 3D skeletal CNN spatiotemporal feature embedded system real-time action recognition Long Short-Term Memory spatio–temporal differential n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Deep Learning-Based Action Recognition |
| title | Deep Learning-Based Action Recognition |
| title_full | Deep Learning-Based Action Recognition |
| title_fullStr | Deep Learning-Based Action Recognition |
| title_full_unstemmed | Deep Learning-Based Action Recognition |
| title_short | Deep Learning-Based Action Recognition |
| title_sort | deep learning based action recognition |
| topic | human action recognition graph convolution high-order feature spatio-temporal feature feature fusion dynamic gesture recognition multi-modalities network class regularization 3D-CNN spatiotemporal activations class-specific features Dynamic Hand Gesture Recognition human-computer interaction hand shape features pose estimation stacked hourglass network deep learning convolutional receptive field hand gesture recognition human–machine interface artificial intelligence feedforward neural networks spatio-temporal image formation human activity recognition fusion strategies transfer learning activity recognition data augmentation multi-person pose estimation partitioned centerpose network partition pose representation continuous hand gesture recognition gesture spotting gesture classification multi-modal features 3D skeletal CNN spatiotemporal feature embedded system real-time action recognition Long Short-Term Memory spatio–temporal differential n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | human action recognition graph convolution high-order feature spatio-temporal feature feature fusion dynamic gesture recognition multi-modalities network class regularization 3D-CNN spatiotemporal activations class-specific features Dynamic Hand Gesture Recognition human-computer interaction hand shape features pose estimation stacked hourglass network deep learning convolutional receptive field hand gesture recognition human–machine interface artificial intelligence feedforward neural networks spatio-temporal image formation human activity recognition fusion strategies transfer learning activity recognition data augmentation multi-person pose estimation partitioned centerpose network partition pose representation continuous hand gesture recognition gesture spotting gesture classification multi-modal features 3D skeletal CNN spatiotemporal feature embedded system real-time action recognition Long Short-Term Memory spatio–temporal differential n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20221025_9783036551999_64 |