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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 101–110 of 419 papers

TitleStatusHype
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation—0
Action Recognition with Multi-stream Motion Modeling and Mutual Information MaximizationCode0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks—0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action RecognitionCode0
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action RecognitionCode1
Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology AwarenessCode1
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition—0
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture RecognitionCode1
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