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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 271280 of 419 papers

TitleStatusHype
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition0
Hierarchical Action Classification with Network Pruning0
Context Aware Graph Convolution for Skeleton-Based Action Recognition0
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition0
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition0
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition0
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition0
Temporal Extension Module for Skeleton-Based Action Recognition0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition0
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