Reprint

Deep Learning-Based Action Recognition

Edited by
September 2022
240 pages
  • ISBN978-3-0365-5199-9 (Hardback)
  • ISBN978-3-0365-5200-2 (PDF)

This 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.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
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