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 161–170 of 419 papers

TitleStatusHype
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for HealthcareCode0
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Investigation of Different Skeleton Features for CNN-based 3D 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
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNNCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Show:102550
← PrevPage 17 of 42Next →

No leaderboard results yet.