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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 191200 of 419 papers

TitleStatusHype
Action Recognition with Domain Invariant Features of Skeleton Image0
A Central Difference Graph Convolutional Operator for Skeleton-Based Action RecognitionCode0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition0
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action RecognitionCode0
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition0
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Sign Language Recognition via Skeleton-Aware Multi-Model EnsembleCode1
Unsupervised Motion Representation Learning with Capsule AutoencodersCode0
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional NetworksCode1
Adversarial Bone Length Attack on Action Recognition0
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