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 1851–1900 of 2184 papers

TitleStatusHype
Learning Representative Temporal Features for Action Recognition—0
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Hierarchical Spatial Transformer Network—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Let's Dance: Learning From Online Dance VideosCode0
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?Code0
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning—0
Combining Stereo Disparity and Optical Flow for Basic Scene Flow—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks—0
Instance Embedding Transfer to Unsupervised Video Object Segmentation—0
A Unified Method for First and Third Person Action Recognition—0
Future Frame Prediction for Anomaly Detection -- A New BaselineCode1
On the Integration of Optical Flow and Action Recognition—0
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research—0
Objects that Sound—0
Im2Flow: Motion Hallucination from Static Images for Action RecognitionCode0
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints—0
Multi-Scale Video Frame-Synthesis Network with Transitive Consistency Loss—0
Vision-Based Fall Detection with Convolutional Neural NetworksCode0
Self-supervised Learning of Motion CaptureCode0
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks—0
Learning to Segment Moving Objects—0
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video InterpolationCode0
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action RecognitionCode0
Hierarchical Video Generation from Orthogonal Information: Optical Flow and TextureCode0
Video Enhancement with Task-Oriented FlowCode1
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census LossCode0
3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints—0
Occlusion Aware Unsupervised Learning of Optical Flow—0
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics—0
End-to-end Video-level Representation Learning for Action RecognitionCode0
Predicting Scene Parsing and Motion Dynamics in the Future—0
Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification—0
End-to-end Flow Correlation Tracking with Spatial-temporal Attention—0
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System—0
Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background SeparationCode0
Sistema de Navegação Autônomo Baseado em Visão Computacional—0
Pose-based Deep Gait Recognition—0
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor—0
Secrets in Computing Optical Flow by Convolutional Networks—0
A 3D Morphable Model of Craniofacial Shape and Texture Variation—0
Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras—0
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?—0
Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations—0
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images—0
Playing for Benchmarks—0
SegFlow: Joint Learning for Video Object Segmentation and Optical FlowCode0
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation—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