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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 311320 of 419 papers

TitleStatusHype
Graph Based Skeleton Modeling for Human Activity Analysis0
Learning stochastic differential equations using RNN with log signature features0
SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action RecognitionCode0
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal AttentionCode0
Spatiotemporal graph routing for skeleton-based action recognition0
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition0
Non-Local Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
A Comparative Review of Recent Kinect-based Action Recognition AlgorithmsCode0
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition0
Bayesian Hierarchical Dynamic Model for Human Action RecognitionCode0
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