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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 251–260 of 419 papers

TitleStatusHype
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition—0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition—0
The Multi-Modal Video Reasoning and Analyzing Competition—0
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition—0
Effective Action Recognition with Embedded Key Point Shifts—0
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks—0
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition—0
Pose Encoding for Robust Skeleton-Based Action Recognition—0
Pose for Action - Action for Pose—0
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition—0
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