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 1801–1850 of 2184 papers

TitleStatusHype
Random Temporal Skipping for Multirate Video Analysis—0
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback—0
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks—0
Real-time Action Recognition with Enhanced Motion Vector CNNs—0
Real-time AdaBoost cascade face tracker based on likelihood map and optical flow—0
Real-Time Anomaly Detection With HMOF Feature—0
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach—0
Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis—0
Real-Time End-to-End Action Detection with Two-Stream Networks—0
Real-Time Human Action Recognition on Embedded Platforms—0
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving—0
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks—0
Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments—0
Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles—0
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking—0
Real-Time Sign Language Detection using Human Pose Estimation—0
Real-time Spatio-temporal Action Localization via Learning Motion Representation—0
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning—0
Recognize Actions by Disentangling Components of Dynamics—0
Rectification-specific Supervision and Constrained Estimator for Online Stereo Rectification—0
Recurrent Convolutional Network for Video-Based Person Re-Identification—0
Recurrent Network Models for Human Dynamics—0
ReFIn: A Refinement Approach for Video Frame Interpolation—0
Regularized Pel-Recursive Motion Estimation Using Generalized Cross-Validation and Spatial Adaptation—0
ReHAR: Robust and Efficient Human Activity Recognition—0
Reinforcement Learning meets Masked Video Modeling : Trajectory-Guided Adaptive Token Selection—0
REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning—0
Removing Multi-frame Gaussian Noise by Combining Patch-based Filters with Optical Flow—0
Research Experience of an Undergraduate Student in Computer Vision and Robotics—0
ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation—0
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching—0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
ResQ: Residual Quantization for Video Perception—0
Restore from Restored: Video Restoration with Pseudo Clean Video—0
Rethinking Atmospheric Turbulence Mitigation—0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations—0
Revisiting Document Image Dewarping by Grid Regularization—0
Revisiting Event-based Video Frame Interpolation—0
Revisiting Optical Flow Estimation in 360 Videos—0
RipViz: Finding Rip Currents by Learning Pathline Behavior—0
Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data—0
Robot Localization and Mapping Final Report -- Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidance—0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks—0
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow—0
Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes—0
Robust Instance Tracking via Uncertainty Flow—0
Robust Isometric Non-Rigid Structure-from-Motion—0
Robust Monocular Epipolar Flow Estimation—0
Show:102550
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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