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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

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Showing 371–380 of 419 papers

TitleStatusHype
Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action RecognitionCode0
UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildCode0
Domain and View-point Agnostic Hand Action RecognitionCode0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action RecognitionCode0
Joint-Partition Group Attention for skeleton-based action recognitionCode0
Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video SurveillanceCode0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
Attack-Augmentation Mixing-Contrastive Skeletal Representation LearningCode0
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