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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 121130 of 419 papers

TitleStatusHype
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition0
3D Skeleton-Based Action Recognition: A Review0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition0
BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation0
HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition0
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for HealthcareCode0
Improving Skeleton-based Action Recognition with Interactive Object InformationCode0
Evolving Skeletons: Motion Dynamics in Action Recognition0
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