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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
View invariant human action recognition using histograms of 3D joints—0
Focalized Contrastive View-invariant Learning for Self-supervised 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
Adaptive RNN Tree for Large-Scale Human Action Recognition—0
Focal and Global Spatial-Temporal Transformer for Skeleton-based 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
FenceNet: Fine-grained Footwork Recognition in Fencing—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
Evolving Skeletons: Motion Dynamics in Action Recognition—0
Temporal Extension Module for Skeleton-Based Action Recognition—0
Temporal Graph Modeling for Skeleton-based Action Recognition—0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition—0
Object Activity Scene Description, Construction and Recognition—0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
Enhanced skeleton visualization for view invariant human action recognition—0
PA3D: Pose-Action 3D Machine for Video Recognition—0
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition—0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
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