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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 171180 of 419 papers

TitleStatusHype
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks0
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition0
Action-Attending Graphic Neural Network0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition0
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition0
Joint Temporal Pooling for Improving Skeleton-based Action Recognition0
Deep Learning on Lie Groups for Skeleton-based Action Recognition0
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark0
Modeling Video Evolution for Action Recognition0
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