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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 281290 of 419 papers

TitleStatusHype
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner0
Towards a Skeleton-Based Action Recognition For Realistic Scenarios0
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition0
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data0
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition0
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching0
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition0
Self-attention based anchor proposal for skeleton-based action recognition0
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