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Optical Flow Estimation
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Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Further readings:
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/amogh7joshi/media-vision
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
https://github.com/anchen1011/toflow
https://github.com/andycao1125/mambadm
https://github.com/ankitAMD/Optical-Flow_Python
https://github.com/antonilo/unsupervised_detection
Unsupervised Moving Object Detection via Contextual Information Separation
https://github.com/antoninofurnari/rulstm
https://github.com/antonymarion/fit_and_compare
https://github.com/anuragranj/flowattack
https://github.com/anuragranj/humanflow2
https://github.com/arashk7/DispNet_Keras
https://github.com/ashajkofci/flowestimation
Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep Learning
https://github.com/avinashpaliwal/Super-SloMo
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/ayushraj7/Video-Anomaly-Detection
https://github.com/baowenbo/MEMC-Net
https://github.com/bbldCVer/EDEN
EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation
https://github.com/best-of-acrv/toposlam
https://github.com/bestaar/predictiveFilterFlow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
https://github.com/binzzheng/DVC-PyTorch
https://github.com/bkvie/Locally-Consistent-Deformable-Convolution
https://github.com/bryanyzhu/two-stream-pytorch
Hidden Two-Stream Convolutional Networks for Action Recognition
https://github.com/cardwing/Codes-for-Steering-Control
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
https://github.com/carlo-/sepconv-ios
Video Frame Interpolation via Adaptive Separable Convolution
https://github.com/cfreshgirl/uflow
https://github.com/chingswy/HumanPoseMemo
https://github.com/christy1206/STSTNet
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
https://github.com/chunfeng0301/Flow
https://github.com/cirpote/AgriColMap
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
https://github.com/code-implementation1/Code1/tree/main/dgcnet_res101
https://github.com/code-implementation1/Code6/tree/main/PWCNet
Normalized Convolution Upsampling for Refined Optical Flow Estimation
https://github.com/coupriec/VideoPredictionICLR2016
https://github.com/cufctl/segmentation
https://github.com/cv-stuttgart/springwebsite
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
https://github.com/daigo0927/PWC-Net_tf
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/daigo0927/pwcnet
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/dajes/frame-interpolation-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.