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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 91100 of 419 papers

TitleStatusHype
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
PREDICT & CLUSTER: Unsupervised Skeleton Based Action RecognitionCode1
A Spatio-Temporal Multilayer Perceptron for Gesture RecognitionCode1
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse OcclusionsCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action RecognitionCode1
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