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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 110 of 419 papers

TitleStatusHype
MotionBERT: A Unified Perspective on Learning Human Motion RepresentationsCode3
BlockGCN: Redefine Topology Awareness for Skeleton-Based Action RecognitionCode2
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action RecognitionCode2
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action RecognitionCode2
SkateFormer: Skeletal-Temporal Transformer for Human Action RecognitionCode2
Hulk: A Universal Knowledge Translator for Human-Centric TasksCode2
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
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