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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 231240 of 419 papers

TitleStatusHype
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition0
ADG-Pose: Automated Dataset Generation for Real-World Human Pose EstimationCode0
Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video SurveillanceCode0
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition0
Self-attention based anchor proposal for skeleton-based action recognition0
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition0
Action Recognition with Domain Invariant Features of Skeleton Image0
A Central Difference Graph Convolutional Operator for Skeleton-Based Action RecognitionCode0
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