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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
Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature AlignmentCode1
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition0
3D Skeleton-Based Action Recognition: A Review0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature AggregationCode1
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language RecognitionCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition0
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
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