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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 221230 of 419 papers

TitleStatusHype
Skeleton-based Action Recognition via Adaptive Cross-Form LearningCode0
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition0
PYSKL: Towards Good Practices for Skeleton Action Recognition0
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark0
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints InvarianceCode0
FenceNet: Fine-grained Footwork Recognition in Fencing0
PYSKL: a toolbox for skeleton-based video understanding0
SpatioTemporal Focus for Skeleton-based Action Recognition0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition0
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