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/ducongju/HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/dvl-tum/motsynth-baselines
https://github.com/dvlab-research/msad
https://github.com/eho-tacc/DeepLabCut
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/eldar/deepcut
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/eldar/deepcut-cnn
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/eldar/pose-tensorflow
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/elonashatri/pitch_mask_rcnn
https://github.com/erezposner/HandKeyPointDetector
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
https://github.com/eric-erki/pose-residual-network-pytorch
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
https://github.com/estelabalboa/Proyecto_Final_Pilates
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/ethnhe/FFB6D
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
https://github.com/evaristr/py-faster_rcnn
https://github.com/fabio-sim/DeDoDe-ONNX-TensorRT
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
https://github.com/facebookresearch/DetectAndTrack
https://github.com/facebookresearch/SlowFast
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/detectron2
https://github.com/facebookresearch/maskrcnn-benchmark
https://github.com/facebookresearch/sapiens
https://github.com/fcr3/OpenVINO_Tutorials
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/fcr3/wine_detector
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/fdac18/ForensicImages
https://github.com/fenglinglwb/MSPN
Rethinking on Multi-Stage Networks for Human Pose Estimation
https://github.com/fengyidong123/openpose-master
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/fiveai/saod
https://github.com/franknb/Self-attention-DCGAN
https://github.com/frgfm/Holocron
https://github.com/fsafe/Capstone
https://github.com/geopavlakos/object3d
https://github.com/gfjiangly/CenterNet
https://github.com/gitting-it-right/gaussian_centernet
https://github.com/godspeedcurry/lung-nodule-detection
https://github.com/goodxue/CenterNet
https://github.com/google-coral/project-posenet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.