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
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringCode0
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Attack-Augmentation Mixing-Contrastive Skeletal Representation LearningCode0
Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity RecognitionCode0
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
Hierarchical growing grid networks for skeleton based action recognitionCode0
Show:102550
← PrevPage 20 of 42Next →

No leaderboard results yet.