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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 321330 of 419 papers

TitleStatusHype
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning0
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition0
Action Machine: Rethinking Action Recognition in Trimmed Videos0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition0
3D Skeleton-Based Action Recognition: A Review0
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition0
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition0
Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)0
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks0
Unsupervised representation learning with long-term dynamics for skeleton based action recognition0
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