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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 241250 of 419 papers

TitleStatusHype
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition0
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition0
Object Activity Scene Description, Construction and Recognition0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation0
Enhanced skeleton visualization for view invariant human action recognition0
PA3D: Pose-Action 3D Machine for Video Recognition0
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks0
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