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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 231240 of 419 papers

TitleStatusHype
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition0
FenceNet: Fine-grained Footwork Recognition in Fencing0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition0
Neural Graph Matching Networks for Fewshot 3D Action Recognition0
Evolving Skeletons: Motion Dynamics in Action Recognition0
Temporal Extension Module for Skeleton-Based Action Recognition0
Temporal Graph Modeling for Skeleton-based Action Recognition0
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