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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 1551–1600 of 2184 papers

TitleStatusHype
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow—0
Video Interpolation using Optical Flow and Laplacian Smoothness—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Video Matting via Consistency-Regularized Graph Neural Networks—0
Video Modeling with Correlation Networks—0
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions—0
Video Person Re-Identification using Learned Clip Similarity Aggregation—0
Texture-Based Input Feature Selection for Action Recognition—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Video Salient Object Detection via Fully Convolutional Networks—0
Video Segmentation via Object Flow—0
Video shutter angle estimation using optical flow and linear blur—0
Video-Story Composition via Plot Analysis—0
Violence detection in videos using deep recurrent and convolutional neural networks—0
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models—0
ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization—0
Virtual Piano using Computer Vision—0
Virtual Worlds as Proxy for Multi-Object Tracking Analysis—0
Vision-based control for landing an aerial vehicle on a marine vessel—0
Vision-based localization methods under GPS-denied conditions—0
Visual Descriptor Learning from Monocular Video—0
Visual Looming from Motion Field and Surface Normals—0
Visually Guided Sound Source Separation and Localization using Self-Supervised Motion Representations—0
Visual Rhythm Prediction with Feature-Aligning Network—0
Visuomotor Understanding for Representation Learning of Driving Scenes—0
Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations—0
Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy—0
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow—0
Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency—0
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition—0
Weakly Supervised Video Salient Object Detection via Point Supervision—0
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks—0
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors—0
What If We Do Not Have Multiple Videos of the Same Action? -- Video Action Localization Using Web Images—0
Disentangling Architecture and Training for Optical Flow—0
What Matters in Detecting AI-Generated Videos like Sora?—0
What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection—0
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images—0
WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs—0
Win-Win: Training High-Resolution Vision Transformers from Two Windows—0
WorldGen: A Large Scale Generative Simulator—0
X Modality Assisting RGBT Object Tracking—0
X-ray Multimodal Intrinsic-Speckle-Tracking—0
XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications—0
YOIO: You Only Iterate Once by mining and fusing multiple necessary global information in the optical flow estimation—0
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks—0
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark—0
Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark—0
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models—0
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination—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