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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 181–190 of 419 papers

TitleStatusHype
Spatio-Temporal Tuples Transformer for Skeleton-Based Action RecognitionCode1
InfoGCN: Representation Learning for Human Skeleton-Based Action RecognitionCode1
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve—0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition—0
Self-attention based anchor proposal for skeleton-based action recognition—0
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action RecognitionCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition—0
Anonymization for Skeleton Action RecognitionCode1
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
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