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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 226–250 of 419 papers

TitleStatusHype
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning—0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition—0
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition—0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition—0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
Modeling Video Evolution for Action Recognition—0
Motion feature augmented network for dynamic hand gesture recognition from skeletal data—0
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition—0
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition—0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition—0
Multi-region two-stream R-CNN for action detection—0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition—0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
Object Activity Scene Description, Construction and Recognition—0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
PA3D: Pose-Action 3D Machine for Video Recognition—0
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition—0
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition—0
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition—0
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks—0
Pose Encoding for Robust Skeleton-Based Action Recognition—0
Pose for Action - Action for Pose—0
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition—0
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