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Skeleton Based Action Recognition

Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.

( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )

Papers

Showing 371380 of 419 papers

TitleStatusHype
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition0
Adversarial Bone Length Attack on Action Recognition0
Joint Temporal Pooling for Improving Skeleton-based Action Recognition0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition0
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks0
Hierarchical recurrent neural network for skeleton based action recognition0
Hierarchical Action Classification with Network Pruning0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition0
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