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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 381390 of 419 papers

TitleStatusHype
Relational Autoencoder for Feature ExtractionCode0
Joint Mixing Data Augmentation for Skeleton-based Action RecognitionCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete SkeletonsCode0
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action RecognitionCode0
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in VideosCode0
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for HealthcareCode0
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action RecognitionCode0
3D CNNs on Distance Matrices for Human Action RecognitionCode0
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