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 1551–1600 of 2184 papers

TitleStatusHype
PSNet: Parallel Symmetric Network for Video Salient Object Detection—0
Pūioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System—0
Pushing the Limits of Deep CNNs for Pedestrian Detection—0
Autonomous Satellite Detection and Tracking using Optical Flow—0
Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes—0
3D Velocity from 3D Doppler Radial Velocity—0
Pyramidal Gradient Matching for Optical Flow Estimation—0
Qiniu Submission to ActivityNet Challenge 2018—0
Quadcopter Position Hold Function using Optical Flow in a Smartphone-based Flight Computer—0
Quadrotor going through a window and landing: An image-based visual servo control approach—0
Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model—0
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel—0
Automatic Quantification of Facial Asymmetry using Facial Landmarks—0
RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer—0
Automatic Portrait Video Matting via Context Motion Network—0
Automatic learning of gait signatures for people identification—0
RainFlow: Optical Flow Under Rain Streaks and Rain Veiling Effect—0
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos—0
Random Temporal Skipping for Multirate Video Analysis—0
Automatic Action Annotation in Weakly Labeled Videos—0
AutoDepthNet: High Frame Rate Depth Map Reconstruction using Commodity Depth and RGB Cameras—0
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback—0
3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints—0
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks—0
X Modality Assisting RGBT Object Tracking—0
A Universal Railway Obstacle Detection System based on Semi-supervised Segmentation And Optical Flow—0
Video Desnowing and Deraining Based on Matrix Decomposition—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
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking—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
Video Diffusion Models are Strong Video Inpainter—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
A Unified Method for First and Third Person Action Recognition—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
Audio-Visual Target Speaker Enhancement on Multi-Talker Environment using Event-Driven Cameras—0
Recognize Actions by Disentangling Components of Dynamics—0
3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints—0
Rectification-specific Supervision and Constrained Estimator for Online Stereo Rectification—0
Recurrent Convolutional Network for Video-Based Person Re-Identification—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