SOTAVerified

Optical Flow Estimation

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

Papers

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Showing 601–650 of 2184 papers

TitleStatusHype
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness—0
Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features—0
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies—0
3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints—0
Feasibility of Video-based Sub-meter Localization on Resource-constrained Platforms—0
Deep Motion Prior for Weakly-Supervised Temporal Action Localization—0
Deep Motion Features for Visual Tracking—0
A Video-Based Method for Objectively Rating Ataxia—0
A Video-Based Activity Classification of Human Pickers in Agriculture—0
Deep Motion Boundary Detection—0
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos—0
2D LiDAR Map Prediction via Estimating Motion Flow with GRU—0
Feature-Aligned Video Raindrop Removal with Temporal Constraints—0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis—0
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles—0
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network—0
AutoScaler: Scale-Attention Networks for Visual Correspondence—0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow—0
Action Recognition in the Frequency Domain—0
Autonomous Satellite Detection and Tracking using Optical Flow—0
A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination—0
VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal—0
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition—0
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos—0
Deep Learning Enabled Semantic Communication Systems for Video Transmission—0
Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes—0
Deep learning based fence segmentation and removal from an image using a video sequence—0
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery—0
3D Scene Flow from 4D Light Field Gradients—0
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Automatic Quantification of Facial Asymmetry using Facial Landmarks—0
Deep Iterative 2D/3D Registration—0
DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction—0
Automatic Portrait Video Matting via Context Motion Network—0
Video-based Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes—0
Deep Frame Interpolation—0
Automatic learning of gait signatures for people identification—0
ActionFlowNet: Learning Motion Representation for Action Recognition—0
Deep Flow Collaborative Network for Online Visual Tracking—0
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation—0
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos—0
Deep End2End Voxel2Voxel Prediction—0
Algorithm for the reconstruction of dynamic objects in CT-scanning using optical flow—0
Deep Dynamic Scene Deblurring from Optical Flow—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Automatic Action Annotation in Weakly Labeled Videos—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64—Unverified
2FastFlowNet-ftAverage End-Point Error4.89—Unverified
3UnrolledCostAverage End-Point Error4.69—Unverified
4LiteFlowNet-ftAverage End-Point Error4.54—Unverified
5FlowNet2Average End-Point Error3.96—Unverified
6IRR-PWCAverage End-Point Error3.84—Unverified
7SelFlowAverage End-Point Error3.74—Unverified
8FDFlowNet-ftAverage End-Point Error3.71—Unverified
9ScopeFlowAverage End-Point Error3.59—Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48—Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36—Unverified
2FastFlowNet-ftAverage End-Point Error6.08—Unverified
3UnrolledCostAverage End-Point Error5.8—Unverified
4MR-FlowAverage End-Point Error5.38—Unverified
5LiteFlowNet-ftAverage End-Point Error5.38—Unverified
6FDFlowNet-ftAverage End-Point Error5.11—Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69—Unverified
8IRR-PWCAverage End-Point Error4.58—Unverified
9LiteFlowNet3-SAverage End-Point Error4.53—Unverified
10ContinualFlow + ftAverage End-Point Error4.52—Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7—Unverified
2FastFlowNetF1-all33.1—Unverified
3FlowNet2F1-all30—Unverified
4VCNF1-all25.1—Unverified
5HD3F1-all24—Unverified
6MaskFlowNetF1-all23.1—Unverified
7SCVF1-all19.3—Unverified
8RAPIDFlowF1-all17.7—Unverified
9CRAFTF1-all17.5—Unverified
10RAFTF1-all17.4—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22—Unverified
2UnrolledCostFl-all10.81—Unverified
3LiteFlowNet-ftFl-all9.38—Unverified
4SelFlowFl-all8.42—Unverified
5IRR-PWCFl-all7.65—Unverified
6LiteFlowNet2-ftFl-all7.62—Unverified
7LiteFlowNet3Fl-all7.34—Unverified
8LiteFlowNet3-SFl-all7.22—Unverified
9MaskFlownet-SFl-all6.81—Unverified
10RAPIDFlowFl-all6.12—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8—Unverified
2LiteFlowNet-ftAverage End-Point Error1.6—Unverified
3IRR-PWCAverage End-Point Error1.6—Unverified
4SelFlowAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3-SAverage End-Point Error1.3—Unverified
9LiteFlowNet3Average End-Point Error1.3—Unverified
10MaskFlownetAverage End-Point Error1.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27—Unverified
2SPyNet1px total29.96—Unverified
3GMFlow1px total10.36—Unverified
4GMA1px total7.07—Unverified
5RAFT1px total6.79—Unverified
6FlowNet21px total6.71—Unverified
7FlowFormer1px total6.51—Unverified
8MS-RAFT+1px total5.72—Unverified
9RPKNet1px total4.81—Unverified
10DPFlow1px total3.44—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21—Unverified
2MDFlow-FastAverage End-Point Error4.73—Unverified
3UpFlowAverage End-Point Error4.68—Unverified
4ARFlow-MVAverage End-Point Error4.49—Unverified
5MDFlowAverage End-Point Error4.16—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5—Unverified
2MDFlow-FastAverage End-Point Error5.99—Unverified
3ARFlow-MVAverage End-Point Error5.67—Unverified
4MDFlowAverage End-Point Error5.46—Unverified
5UpFlowAverage End-Point Error5.32—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79—Unverified
2MDFlow-FastFl-all11.43—Unverified
3UpFlowFl-all9.38—Unverified
4MDFlowFl-all8.91—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5—Unverified
2UpFlowAverage End-Point Error1.4—Unverified