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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 351360 of 419 papers

TitleStatusHype
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition0
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition0
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition0
SpatioTemporal Focus for Skeleton-based Action Recognition0
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition0
Spatiotemporal graph routing for skeleton-based action recognition0
A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition0
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition0
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