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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
PA3D: Pose-Action 3D Machine for Video Recognition0
Bayesian Hierarchical Dynamic Model for Human Action RecognitionCode0
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete SkeletonsCode0
Towards a Skeleton-Based Action Recognition For Realistic Scenarios0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition0
Simple yet efficient real-time pose-based action recognitionCode0
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action RecognitionCode0
Multigrid Predictive Filter Flow for Unsupervised Learning on VideosCode0
STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection0
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