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

Showing 401410 of 419 papers

TitleStatusHype
Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringCode0
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data0
NTU RGB+D: A Large Scale Dataset for 3D Human Activity AnalysisCode0
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks0
Pose for Action - Action for Pose0
Structural-RNN: Deep Learning on Spatio-Temporal GraphsCode0
Hierarchical recurrent neural network for skeleton based action recognition0
Modeling Video Evolution for Action Recognition0
Joint Action Recognition and Pose Estimation From Video0
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition0
Show:102550
← PrevPage 41 of 42Next →

No leaderboard results yet.