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 281–290 of 419 papers

TitleStatusHype
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information—0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition—0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method—0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks—0
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition—0
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition—0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition—0
Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition—0
Self-Attention Network for Skeleton-based Human Action Recognition—0
Show:102550
← PrevPage 29 of 42Next →

No leaderboard results yet.