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 701–750 of 2184 papers

TitleStatusHype
Crowd Flow Segmentation in Compressed Domain using CRF—0
Attacking Motion Estimation with Adversarial Snow—0
Cross Pixel Optical Flow Similarity for Self-Supervised Learning—0
Fast video object segmentation with Spatio-Temporal GANs—0
Cross-Modal Message Passing for Two-stream Fusion—0
A Hybrid Approach To Real-Time Multi-Object Tracking—0
FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation—0
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution—0
FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving—0
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition—0
Cross domain knowledge compression in realtime optical flow prediction on ultrasound sequences—0
ATM: Action Temporality Modeling for Video Question Answering—0
Frame Interpolation Transformer and Uncertainty Guidance—0
ATG-PVD: Ticketing Parking Violations on A Drone—0
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation—0
Fast Event-based Optical Flow Estimation by Triplet Matching—0
Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow—0
Forecasting Future Instance Segmentation with Learned Optical Flow and Warping—0
Fast Convex Relaxations using Graph Discretizations—0
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Fast Optical Flow using Dense Inverse Search—0
Crossing the Road Without Traffic Lights: An Android-based Safety Device—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition—0
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation—0
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution—0
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network—0
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting—0
Feasibility of Video-based Sub-meter Localization on Resource-constrained Platforms—0
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks—0
Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN—0
Feature Hallucination for Self-supervised Action Recognition—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition—0
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information—0
Feature-Supervised Action Modality Transfer—0
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation—0
Tracklets Predicting Based Adaptive Graph Tracking—0
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow—0
Filtered Channel Features for Pedestrian Detection—0
Face Flow—0
Face Animation with Multiple Source Images—0
Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—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