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 391–400 of 419 papers

TitleStatusHype
ADG-Pose: Automated Dataset Generation for Real-World Human Pose EstimationCode0
Deep Learning for Hand Gesture Recognition on Skeletal DataCode0
STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action RecognitionCode0
Deep Independently Recurrent Neural Network (IndRNN)Code0
Structural-RNN: Deep Learning on Spatio-Temporal GraphsCode0
Investigation of Different Skeleton Features for CNN-based 3D Action RecognitionCode0
Structure-Aware Convolutional Neural NetworksCode0
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action RecognitionCode0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action RecognitionCode0
Show:102550
← PrevPage 40 of 42Next →

No leaderboard results yet.