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

Showing 14511500 of 2184 papers

TitleStatusHype
Multi-Modal Domain Adaptation for Fine-Grained Action RecognitionCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
Removing Multi-frame Gaussian Noise by Combining Patch-based Filters with Optical Flow0
The benefits of synthetic data for action categorization0
FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation0
Temporal Interlacing NetworkCode1
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action RecognitionCode1
Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors0
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification0
A Differentiable Recurrent Surface for Asynchronous Event-Based DataCode1
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map0
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC0
Implementation of the VBM3D Video Denoising Method and Some VariantsCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
First image then video: A two-stage network for spatiotemporal video denoisingCode0
Video Depth Estimation by Fusing Flow-to-Depth ProposalsCode0
Efficient Video Semantic Segmentation with Labels Propagation and Refinement0
Depth Extraction from Video Using Non-parametric Sampling0
DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling0
Improving Optical Flow on a Pyramid Level0
Spotting Macro- and Micro-expression Intervals in Long Video SequencesCode1
ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization0
Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs0
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration0
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos0
Machine Learning for Precipitation Nowcasting from Radar Images0
Training Deep SLAM on Single Frames0
Deep motion estimation for parallel inter-frame prediction in video compressionCode0
GLU-Net: Global-Local Universal Network for Dense Flow and CorrespondencesCode1
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition0
Self-supervised Object Motion and Depth Estimation from Video0
Amora: Black-box Adversarial Morphing Attack0
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution0
Kernel learning for visual perceptionCode0
15 Keypoints Is All You Need0
Audio-Visual Target Speaker Enhancement on Multi-Talker Environment using Event-Driven Cameras0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations0
EventGAN: Leveraging Large Scale Image Datasets for Event CamerasCode0
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving0
Volumetric Correspondence Networks for Optical FlowCode1
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow0
Estimating People Flows to Better Count Them in Crowded ScenesCode1
Learning End-To-End Scene Flow by Distilling Single Tasks KnowledgeCode0
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion RecognitionCode0
Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation0
Fast Learning of Temporal Action Proposal via Dense Boundary GeneratorCode1
End to end collision avoidance based on optical flow and neural networks0
Deep Flow Collaborative Network for Online Visual Tracking0
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64Unverified
2FastFlowNet-ftAverage End-Point Error4.89Unverified
3UnrolledCostAverage End-Point Error4.69Unverified
4LiteFlowNet-ftAverage End-Point Error4.54Unverified
5FlowNet2Average End-Point Error3.96Unverified
6IRR-PWCAverage End-Point Error3.84Unverified
7SelFlowAverage End-Point Error3.74Unverified
8FDFlowNet-ftAverage End-Point Error3.71Unverified
9ScopeFlowAverage End-Point Error3.59Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36Unverified
2FastFlowNet-ftAverage End-Point Error6.08Unverified
3UnrolledCostAverage End-Point Error5.8Unverified
4MR-FlowAverage End-Point Error5.38Unverified
5LiteFlowNet-ftAverage End-Point Error5.38Unverified
6FDFlowNet-ftAverage End-Point Error5.11Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69Unverified
8IRR-PWCAverage End-Point Error4.58Unverified
9LiteFlowNet3-SAverage End-Point Error4.53Unverified
10ContinualFlow + ftAverage End-Point Error4.52Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7Unverified
2FastFlowNetF1-all33.1Unverified
3FlowNet2F1-all30Unverified
4VCNF1-all25.1Unverified
5HD3F1-all24Unverified
6MaskFlowNetF1-all23.1Unverified
7SCVF1-all19.3Unverified
8RAPIDFlowF1-all17.7Unverified
9CRAFTF1-all17.5Unverified
10RAFTF1-all17.4Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22Unverified
2UnrolledCostFl-all10.81Unverified
3LiteFlowNet-ftFl-all9.38Unverified
4SelFlowFl-all8.42Unverified
5IRR-PWCFl-all7.65Unverified
6LiteFlowNet2-ftFl-all7.62Unverified
7LiteFlowNet3Fl-all7.34Unverified
8LiteFlowNet3-SFl-all7.22Unverified
9MaskFlownet-SFl-all6.81Unverified
10RAPIDFlowFl-all6.12Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8Unverified
2LiteFlowNet-ftAverage End-Point Error1.6Unverified
3IRR-PWCAverage End-Point Error1.6Unverified
4SelFlowAverage End-Point Error1.5Unverified
5FDFlowNet-ftAverage End-Point Error1.5Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4Unverified
8LiteFlowNet3-SAverage End-Point Error1.3Unverified
9LiteFlowNet3Average End-Point Error1.3Unverified
10MaskFlownetAverage End-Point Error1.1Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27Unverified
2SPyNet1px total29.96Unverified
3GMFlow1px total10.36Unverified
4GMA1px total7.07Unverified
5RAFT1px total6.79Unverified
6FlowNet21px total6.71Unverified
7FlowFormer1px total6.51Unverified
8MS-RAFT+1px total5.72Unverified
9RPKNet1px total4.81Unverified
10DPFlow1px total3.44Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21Unverified
2MDFlow-FastAverage End-Point Error4.73Unverified
3UpFlowAverage End-Point Error4.68Unverified
4ARFlow-MVAverage End-Point Error4.49Unverified
5MDFlowAverage End-Point Error4.16Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5Unverified
2MDFlow-FastAverage End-Point Error5.99Unverified
3ARFlow-MVAverage End-Point Error5.67Unverified
4MDFlowAverage End-Point Error5.46Unverified
5UpFlowAverage End-Point Error5.32Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79Unverified
2MDFlow-FastFl-all11.43Unverified
3UpFlowFl-all9.38Unverified
4MDFlowFl-all8.91Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5Unverified
2UpFlowAverage End-Point Error1.4Unverified