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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 1120 of 419 papers

TitleStatusHype
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
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
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition0
Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence CriterionCode0
Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity RecognitionCode0
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition0
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action RecognitionCode0
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