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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 101110 of 419 papers

TitleStatusHype
Subspace Clustering for Action Recognition with Covariance Representations and Temporal PruningCode1
Iterate & Cluster: Iterative Semi-Supervised Action RecognitionCode1
Skeleton-Based Action Recognition With Shift Graph Convolutional NetworkCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action RecognitionCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
Infrared and 3D skeleton feature fusion for RGB-D action recognitionCode1
UniPose: Unified Human Pose Estimation in Single Images and VideosCode1
PREDICT & CLUSTER: Unsupervised Skeleton Based Action RecognitionCode1
Make Skeleton-based Action Recognition Model Smaller, Faster and BetterCode1
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