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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 331340 of 419 papers

TitleStatusHype
Skeleton-Based Online Action Prediction Using Scale Selection Network0
Relational Network for Skeleton-Based Action Recognition0
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark0
An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition0
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition0
An Improved Graph Pooling Network for Skeleton-Based Action Recognition0
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition0
Skeletonnet: Mining deep part features for 3-d action recognition0
A New Representation of Skeleton Sequences for 3D Action Recognition0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild0
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