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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

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Showing 261–270 of 419 papers

TitleStatusHype
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching—0
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition—0
Pose And Joint-Aware Action RecognitionCode0
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition—0
Effective Action Recognition with Embedded Key Point Shifts—0
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction—0
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