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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 221230 of 419 papers

TitleStatusHype
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton DataCode0
View-Invariant Probabilistic Embedding for Human PoseCode0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition0
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition0
View invariant human action recognition using histograms of 3D joints0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition0
Multi-region two-stream R-CNN for action detection0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition0
Adaptive RNN Tree for Large-Scale Human Action Recognition0
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