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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

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Showing 131–140 of 419 papers

TitleStatusHype
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition—0
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition—0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
Adding Attentiveness to the Neurons in Recurrent Neural Networks—0
Action Machine: Rethinking Action Recognition in Trimmed Videos—0
FenceNet: Fine-grained Footwork Recognition in Fencing—0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation—0
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