Deep Learning-Based Action Recognition

doi:10.3390/books978-3-0365-5200-2

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Deep Learning-Based Action Recognition

Edited by

September 2022

240 pages

ISBN978-3-0365-5199-9 (Hardback)
ISBN978-3-0365-5200-2 (PDF)

https://doi.org/10.3390/books978-3-0365-5200-2

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This book is a reprint of the Special Issue Deep Learning-Based Action Recognition that was published in

Biology & Life Sciences

Chemistry & Materials Science

Computer Science & Mathematics

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

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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Keywords

human action recognition; graph convolution; high-order feature; spatio-temporal feature; feature fusion; dynamic gesture recognition; human action 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; deep learning; fusion strategies; transfer learning; activity recognition; data augmentation; pose estimation; deep learning; multi-person pose estimation; partitioned centerpose network; partition pose representation; continuous hand gesture recognition; gesture spotting; gesture classification; multi-modal features; 3D skeletal; CNN; CNN; human action recognition; spatiotemporal feature; embedded system; real-time; action recognition; Long Short-Term Memory; spatio–temporal differential; n/a