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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/snownus/COOP
https://github.com/soumik12345/point-cloud-segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/stillarrow/S2VT_ACT
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/stnoah1/infogcn
https://github.com/subercui/pyGConvAT
https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/taishan1994/pytorch_gat
https://github.com/tatp22/pytorch-fast-GAT
https://github.com/thanhtrunghuynh93/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/timothylimyl/PointNet-Pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/tk-rusch/graphcon
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/keras-gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tlmakinen/GAT-noise
https://github.com/toinesayan/node-classification-and-label-dependencies
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/trevor-richardson/rnn_zoo
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/tuananh0305/GCN_ANOMALY_DETECTION
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/txyugood/PaddlePoseC3D
https://github.com/udlf/wsef
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/urw7rs/torch_skeleton
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/vailatuts/SSFG-regularization
https://github.com/vijayvee/behavior-recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vijayvee/behavior_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vinits5/learning3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wan2000/mc-mot
https://github.com/weiyangfb/PyTorchSparseGAT
https://github.com/whut2962575697/gat_sementic_segmentation
https://github.com/witignite/Frustum-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wonderland-dsg/pointnet-grid
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wuryantoAji/POINTNET
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wushidonguc/two-stream-action-recognition-keras
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/xiaoiker/GCN-NAS
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
https://github.com/xiuyu0000/new_papers_codes/tree/main/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.