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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 171–180 of 419 papers

TitleStatusHype
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
Joint Mixing Data Augmentation for Skeleton-based Action RecognitionCode0
Joint-Partition Group Attention for skeleton-based action recognitionCode0
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNNCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Non-Local Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D VisionCode0
Improving Skeleton-based Action Recognition with Interactive Object InformationCode0
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
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