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Skeleton Based Action Recognition
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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.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/hassony2/kinetics_i3d_pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hazdzz/ChebyNet
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/hazdzz/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/hazdzz/SGC
https://github.com/helloxy96/CS5242_Project2020
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hhe-distance/AIF-CNN
https://github.com/hnVfly/pointnet.mxnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hnmspirit/pointnet1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/huguyuehuhu/HCN-pytorch
https://github.com/hyperparameters/tracking_via_colorization
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/iamjeff7/j-va-aagcn
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
https://github.com/imanlab/deep_movement_primitives
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/isotlaboratory/ml4vrp
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/jiangboahu/glcn-tf
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/joaopedromattos/gnee
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/kchengiva/Shift-GCN
https://github.com/ken724049/action-recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/kenziyuliu/DGNN-PyTorch
https://github.com/kiharalab/gnn_pocket
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/l13025816/PGCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/lab-midas/lapnet
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lingzhang1/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lingzhang1/pointnet_tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lipingcoding/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/liu6zijian/simplified-gcn-model
https://github.com/liuch37/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/liujf69/TD-GCN-Gesture
https://github.com/lmnt-com/haste
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/lukecavabarrett/pna
https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.