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 1001–1050 of 2184 papers

TitleStatusHype
GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection—0
Deep Motion Features for Visual Tracking—0
A Video-Based Method for Objectively Rating Ataxia—0
Gaze Stabilization for Humanoid Robots: a Comprehensive Framework—0
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation—0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition—0
GateHUB: Gated History Unit with Background Suppression for Online Action Detection—0
Joint Optical Flow and Temporally Consistent Semantic Segmentation—0
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Deep Motion Boundary Detection—0
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection—0
A Video-Based Activity Classification of Human Pickers in Agriculture—0
Blur Removal via Blurred-Noisy Image Pair—0
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos—0
Key frames assisted hybrid encoding for photorealistic compressive video sensing—0
Future frame prediction in chest cine MR imaging using the PCA respiratory motion model and dynamically trained recurrent neural networks—0
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos—0
Future Frame Prediction for Robot-assisted Surgery—0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images—0
Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures—0
Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions—0
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles—0
A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis—0
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments—0
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos—0
Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application—0
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving—0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow—0
AutoScaler: Scale-Attention Networks for Visual Correspondence—0
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation—0
Fully Automated Hand Hygiene Monitoring\ Operating Room using 3D Convolutional Neural Network—0
Full-Velocity Radar Returns by Radar-Camera Fusion—0
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids—0
Autonomous Satellite Detection and Tracking using Optical Flow—0
A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination—0
Action Recognition in the Frequency Domain—0
3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints—0
2D LiDAR Map Prediction via Estimating Motion Flow with GRU—0
FTFDNet: Learning to Detect Talking Face Video Manipulation with Tri-Modality Interaction—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
Deep Learning Enabled Semantic Communication Systems for Video Transmission—0
Deep learning based fence segmentation and removal from an image using a video sequence—0
From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching—0
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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
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