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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 151160 of 419 papers

TitleStatusHype
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action RecognitionCode0
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Multigrid Predictive Filter Flow for Unsupervised Learning on VideosCode0
Action Recognition with Multi-stream Motion Modeling and Mutual Information MaximizationCode0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Deep Learning for Hand Gesture Recognition on Skeletal DataCode0
Deep Independently Recurrent Neural Network (IndRNN)Code0
DeepGRU: Deep Gesture Recognition UtilityCode0
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action RecognitionCode0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
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