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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 161170 of 419 papers

TitleStatusHype
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve0
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method0
Action Capsules: Human Skeleton Action Recognition0
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning0
Modeling Video Evolution for Action Recognition0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition0
D numbers theory based game-theoretic framework in adversarial decision making under fuzzy environment0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences0
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