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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 331340 of 419 papers

TitleStatusHype
Chirality Nets for Human Pose RegressionCode0
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action RecognitionCode0
Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference JointsCode0
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep NetworksCode0
Action Recognition for Privacy-Preserving Ambient Assisted LivingCode0
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action RecognitionCode0
Finding Action TubesCode0
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and DetectionCode0
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