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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 151–160 of 419 papers

TitleStatusHype
Adaptive RNN Tree for Large-Scale Human Action Recognition—0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition—0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition—0
Effective Action Recognition with Embedded Key Point Shifts—0
Early action prediction by soft regression—0
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve—0
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