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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 121130 of 419 papers

TitleStatusHype
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition0
Adversarial Attack on Skeleton-based Human Action Recognition0
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation0
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition0
Jointly learning heterogeneous features for rgb-d activity recognition0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition0
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