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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2050 of 2184 papers

TitleStatusHype
AutoScaler: Scale-Attention Networks for Visual Correspondence—0
Optical Flow Requires Multiple Strategies (but only one network)Code0
Estimating motion with principal component regression strategies—0
Regularized Pel-Recursive Motion Estimation Using Generalized Cross-Validation and Spatial Adaptation—0
Bayesian Optical Flow with Uncertainty QuantificationCode0
Adaptive mixed norm optical flow estimation—0
Temporally coherent completion of dynamic video—0
Joint Large-Scale Motion Estimation and Image Reconstruction—0
Short-term prediction of localized cloud motion using ground-based sky imagers—0
Crossing the Road Without Traffic Lights: An Android-based Safety Device—0
Egocentric Height Estimation—0
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection—0
Deep learning based fence segmentation and removal from an image using a video sequence—0
Pose from Action: Unsupervised Learning of Pose Features based on Motion—0
Making a Case for Learning Motion Representations with Phase—0
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification—0
Motion Representation with Acceleration Images—0
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness—0
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction—0
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos—0
CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss—0
Joint Optical Flow and Temporally Consistent Semantic Segmentation—0
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold RegularisationCode0
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction—0
Dynamical optical flow of saliency maps for predicting visual attention—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—0
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain—0
Point-wise mutual information-based video segmentation with high temporal consistency—0
Less is More: Micro-expression Recognition from Video using Apex Frame—0
What If We Do Not Have Multiple Videos of the Same Action? -- Video Action Localization Using Web Images—0
Video-Story Composition via Plot Analysis—0
Video Segmentation via Object Flow—0
Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical FlowCode0
Force From Motion: Decoding Physical Sensation in a First Person Video—0
Simultaneous Optical Flow and Intensity Estimation From an Event Camera—0
The Global Patch Collider—0
A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Recurrent Convolutional Network for Video-Based Person Re-Identification—0
Dynamic Filter NetworksCode0
Automatic Action Annotation in Weakly Labeled Videos—0
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies—0
Spontaneous vs. Posed smiles - can we tell the difference?—0
Virtual Worlds as Proxy for Multi-Object Tracking Analysis—0
Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection—0
Comparison of Optimization Methods in Optical Flow Estimation—0
Real-time Action Recognition with Enhanced Motion Vector CNNs—0
Learning by tracking: Siamese CNN for robust target association—0
Parametric Object Motion from Blur—0
Long-term Temporal Convolutions for Action RecognitionCode0
Show:102550
← PrevPage 41 of 44Next →
All datasetsSintel-cleanSintel-finalKITTI 2015 (train)KITTI 2015KITTI 2012SpringSintel Clean unsupervisedSintel Final unsupervisedKITTI 2015 unsupervisedKITTI 2012 unsupervised

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
4LiteFlowNet-ftAverage End-Point Error5.38—Unverified
5MR-FlowAverage 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
2IRR-PWCAverage End-Point Error1.6—Unverified
3LiteFlowNet-ftAverage End-Point Error1.6—Unverified
4PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6SelFlowAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3Average End-Point Error1.3—Unverified
9LiteFlowNet3-SAverage End-Point Error1.3—Unverified
10MaskFlownet-SAverage 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