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Human action recognition from various data modalities: A review
Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …
each action. It has a wide range of applications, and therefore has been attracting increasing …
Human action recognition: A taxonomy-based survey, updates, and opportunities
Human action recognition systems use data collected from a wide range of sensors to
accurately identify and interpret human actions. One of the most challenging issues for …
accurately identify and interpret human actions. One of the most challenging issues for …
Infogcn: Representation learning for human skeleton-based action recognition
Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …
intricacies of human behavior because it can handle the complex relationships between …
Channel-wise topology refinement graph convolution for skeleton-based action recognition
Graph convolutional networks (GCNs) have been widely used and achieved remarkable
results in skeleton-based action recognition. In GCNs, graph topology dominates feature …
results in skeleton-based action recognition. In GCNs, graph topology dominates feature …
Skeleton-based action recognition with shift graph convolutional network
Action recognition with skeleton data is attracting more attention in computer vision.
Recently, graph convolutional networks (GCNs), which model the human body skeletons as …
Recently, graph convolutional networks (GCNs), which model the human body skeletons as …
Vision-based human activity recognition: a survey
Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …
human activities using acquired information from various types of sensors. Although several …
Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding
Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …
demonstrated the effectiveness of 3D representation for action recognition. The existing …
Temporal decoupling graph convolutional network for skeleton-based gesture recognition
Skeleton-based gesture recognition methods have achieved high success using Graph
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …
Decoupling gcn with dropgraph module for skeleton-based action recognition
In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved
remarkable success. Nevertheless, how to efficiently model the spatial-temporal skeleton …
remarkable success. Nevertheless, how to efficiently model the spatial-temporal skeleton …
An attention enhanced graph convolutional lstm network for skeleton-based action recognition
Skeleton-based action recognition is an important task that requires the adequate
understanding of movement characteristics of a human action from the given skeleton …
understanding of movement characteristics of a human action from the given skeleton …