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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 221–230 of 419 papers

TitleStatusHype
Learning stochastic differential equations using RNN with log signature features—0
LLMs are Good Action Recognizers—0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions—0
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning—0
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning—0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition—0
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition—0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition—0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
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