Keypoint Detection
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Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/victimsnino/Simple-Baselines-for-Human-Pose-Estimation-and-Tracking-sample-
https://github.com/victimsnino/pose-simple-baselines-demo.pytorch.
https://github.com/vietanhdev/centernet-bdd-data
https://github.com/vietanhdev/centernet-bdd-data-onnx-conversion
https://github.com/vietanhdev/open-adas
https://github.com/vimalabs/VIMA
https://github.com/visionNoob/hrnet_pytorch
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vitae-transformer/qformer
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
https://github.com/voidrank/SaccadeNet
https://github.com/w-sugar/prtr
https://github.com/wL1u/CenterNet-VisDrone
https://github.com/wanglaotou/centernet_ncnn
https://github.com/waspinator/deep-learning-explorer
https://github.com/wsjzha/deep-high-resolution-net.pytorch
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/xaoch/rapJetson2
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/xar47x/pose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/xggIoU/centernet_tensorflow_wilderface_voc
https://github.com/xiaopengsu/CenterNet
https://github.com/xiaozeguo123/-Quick-Capture-system-HSR
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ximilar-com/xcenternet
https://github.com/xiuyu0000/papers_with_examples/tree/main/maskrcnn
https://github.com/xiuyu0000/papers_with_examples/tree/main/mspn
Rethinking on Multi-Stage Networks for Human Pose Estimation
https://github.com/xjnpark/ds
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/PolarizedSelfAttention.py
Polarized Self-Attention: Towards High-quality Pixel-wise Regression
https://github.com/xuannianz/keras-CenterNet
https://github.com/xunhen/py-faster-rcnn-wjc
https://github.com/xzabg/faster-rcnn-with-Caltech
https://github.com/yangdashi888/CenterNet-windows
https://github.com/yangsenius/Pose-Neural-Fabrics-Search
https://github.com/yangyucheng000/AlphaPose
https://github.com/yangyucheng000/ArtTrack
https://github.com/yangyucheng000/MSPN
Rethinking on Multi-Stage Networks for Human Pose Estimation
https://github.com/yangyucheng000/Mask-RCNN
https://github.com/yangyucheng000/maskrcnn_mobilenetv1
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.