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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 311320 of 419 papers

TitleStatusHype
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition0
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition0
View invariant human action recognition using histograms of 3D joints0
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning0
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition0
Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment0
Hierarchical growing grid networks for skeleton based action recognitionCode0
Skeleton-based Action Recognition via Adaptive Cross-Form LearningCode0
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal AttentionCode0
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