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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 126–150 of 419 papers

TitleStatusHype
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition—0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation—0
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition—0
Learning stochastic differential equations using RNN with log signature features—0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition—0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition—0
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition—0
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition—0
Adding Attentiveness to the Neurons in Recurrent Neural Networks—0
Action Machine: Rethinking Action Recognition in Trimmed Videos—0
FenceNet: Fine-grained Footwork Recognition in Fencing—0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation—0
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation—0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
Evolving Skeletons: Motion Dynamics in Action Recognition—0
Learning Human Activities and Object Affordances from RGB-D Videos—0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition—0
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition—0
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition—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
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