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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 391–400 of 419 papers

TitleStatusHype
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction—0
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation—0
Learning stochastic differential equations using RNN with log signature features—0
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition—0
Graph Based Skeleton Modeling for Human Activity Analysis—0
LLMs are Good Action Recognizers—0
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction—0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
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