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/pranoyr/pose-estimation-with-transformers
End-to-End Trainable Multi-Instance Pose Estimation with Transformers
https://github.com/princeton-vl/pose-ae-train
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
https://github.com/prstrive/CondConv-tensorflow
https://github.com/pvdhove/owl-mask-rcnn
https://github.com/qdhill/openpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/qilei123/pyfasterrcnn
https://github.com/qq330488563/TEST
https://github.com/quangvy2703/ABCNet-ESRGAN-SRTEXT
https://github.com/quic/aimet-model-zoo
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/quocdat32461997/Mask_RCNN
https://github.com/radioactiverockets/OpenPoseBreackoutGame
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ravenwritingdesk/py-faster-rcnn-master
https://github.com/raymon-tian/hourglass-facekeypoints-detection
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
https://github.com/rh01/fast-rnn
https://github.com/rh01/faster-rcnn
https://github.com/rickyHong/py-faster-rcnn-repl
https://github.com/rickyHong/py-faster-rcnn-repl-cudnn5-support
https://github.com/rijuldhir/TSM
https://github.com/rongakowang/densemutualattention
https://github.com/rootAkash/vision.ai
https://github.com/saadbutt32/Conversion-of-Pakistan-Sign-Languag-into-Text-and-Speech-using-OpenPose-and-Machine-Learning
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/saadbutt32/Pakistan-Sign-Language-Interpreter
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/saehan-choi/pixellib_auto_labelling
https://github.com/salihkaragoz/pose-residual-network-pytorch
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
https://github.com/samsh19/ML_project
https://github.com/samson6460/tf2_pose_estimation
https://github.com/sananand007/Xrelab
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/sbetageri/MaskRCNN
https://github.com/scolocke/Traffic_Sign_ID_GTSRB_GTSDB
https://github.com/sdkchris/Exercise-Sheet-1
https://github.com/sdll/hrnet-pose-estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/see--/keras-centernet
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/shihenw/convolutional-pose-machines-release
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/simochen/flowtrack.pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.