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 1451–1500 of 2184 papers

TitleStatusHype
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation—0
The Error is the Feature: how to Forecast Lightning using a Model Prediction Error—0
Improving Optical Flow on a Pyramid Level—0
The Global Patch Collider—0
The U-Net based GLOW for Optical-Flow-free Video Interframe Generation—0
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
Time-based Mapping of Space Using Visual Motion Invariants—0
Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera—0
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion—0
TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation—0
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network—0
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes—0
Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction—0
Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling—0
Towards Mobile Sensing with Event Cameras on High-agility Resource-constrained Devices: A Survey—0
Towards Visual Ego-motion Learning in Robots—0
Track, Check, Repeat: An EM Approach to Unsupervised Tracking—0
Tracking in Crowd is Challenging: Analyzing Crowd based on Physical Characteristics—0
Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009—0
Tracking of the Internal Jugular Vein in Ultrasound Images Using Optical Flow—0
Tracking Virtual Meetings in the Wild: Re-identification in Multi-Participant Virtual Meetings—0
Training Datasets Generation for Machine Learning: Application to Vision Based Navigation—0
Training Deep SLAM on Single Frames—0
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition—0
TransFlow: Transformer as Flow Learner—0
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation—0
Transframer: Arbitrary Frame Prediction with Generative Models—0
Trear: Transformer-based RGB-D Egocentric Action Recognition—0
Triple-stream Deep Metric Learning of Great Ape Behavioural Actions—0
Trustworthy Self-Attention: Enabling the Network to Focus Only on the Most Relevant References—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence—0
Two-dimensional flow field measurement of sediment-laden flow based on ultrasound image velocimetry—0
Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification—0
Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track—0
Two-stream Fusion Model for Dynamic Hand Gesture Recognition using 3D-CNN and 2D-CNN Optical Flow guided Motion Template—0
Two-stream Multi-dimensional Convolutional Network for Real-time Violence Detection—0
Two-Stream Region Convolutional 3D Network for Temporal Activity Detection—0
Rip Current Detection in Nearshore Areas through UAV Video Analysis with Almost Local-Isometric Embedding Techniques on Sphere—0
UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation—0
UFM: A Simple Path towards Unified Dense Correspondence with Flow—0
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance—0
Uncertainty-Aware Human Mesh Recovery From Video by Learning Part-Based 3D Dynamics—0
Uncertainty-Driven Dense Two-View Structure from Motion—0
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements—0
Understanding Crowd Flow Movements Using Active-Langevin Model—0
Understanding Deformable Alignment in Video Super-Resolution—0
Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation—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