SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 191–200 of 419 papers

TitleStatusHype
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition—0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation—0
Learning stochastic differential equations using RNN with log signature features—0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition—0
Show:102550
← PrevPage 20 of 42Next →

No leaderboard results yet.