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Deep Learning-Based Action Recognition
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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
- 3D skeletal
- 3D-CNN
- action recognition
- activity recognition
- artificial intelligence
- class regularization
- class-specific features
- CNN
- continuous hand gesture recognition
- convolutional receptive field
- data augmentation
- deep learning
- dynamic gesture recognition
- Dynamic Hand Gesture Recognition
- embedded system
- feature fusion
- feedforward neural networks
- fusion strategies
- gesture classification
- gesture spotting
- graph convolution
- hand gesture recognition
- hand shape features
- high-order feature
- History of engineering & technology
- human action recognition
- human activity recognition
- Human-computer interaction
- human–machine interface
- long short-term memory
- multi-modal features
- multi-modalities network
- multi-person pose estimation
- n/a
- partition pose representation
- partitioned centerpose network
- pose estimation
- Real-time
- spatio-temporal feature
- spatio-temporal image formation
- spatiotemporal activations
- spatiotemporal feature
- spatio–temporal differential
- stacked hourglass network
- Technology, engineering, agriculture
- Technology: general issues
- transfer learning
Links
DOI: 10.3390/books978-3-0365-5200-2Editions