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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 241250 of 419 papers

TitleStatusHype
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
Graph Convolution with Low-rank Learnable Local FiltersCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks0
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features0
Traffic Control Gesture Recognition for Autonomous VehiclesCode1
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition0
Hierarchical Action Classification with Network Pruning0
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
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