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 351360 of 419 papers

TitleStatusHype
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition0
PoTion: Pose MoTion Representation for Action Recognition0
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition0
Deep Learning for Hand Gesture Recognition on Skeletal DataCode0
Relational Network for Skeleton-Based Action Recognition0
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning0
Object Activity Scene Description, Construction and Recognition0
Unsupervised representation learning with long-term dynamics for skeleton based action recognition0
Show:102550
← PrevPage 36 of 42Next →

No leaderboard results yet.