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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 221–230 of 419 papers

TitleStatusHype
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Action Recognition with Kernel-based Graph Convolutional Networks—0
Tensor Representations for Action RecognitionCode1
Temporal Graph Modeling for Skeleton-based Action Recognition—0
Spatial Temporal Transformer Network for Skeleton-based Action RecognitionCode1
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
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