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 1401–1450 of 2184 papers

TitleStatusHype
Tracking in Crowd is Challenging: Analyzing Crowd based on Physical Characteristics—0
Optical Flow and Mode Selection for Learning-based Video Coding—0
A Novel Indoor Positioning System for unprepared firefighting scenarios—0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction—0
Polarized Optical-Flow Gyroscope—0
3D Fluid Flow Reconstruction Using Compact Light Field PIV—0
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation—0
Self-supervised Motion Representation via Scattering Local Motion Cues—0
Is Sharing of Egocentric Video Giving Away Your Biometric Signature?—0
Unsupervised Learning of Optical Flow with Deep Feature Similarity—0
Change Detection Using Synthetic Aperture Radar Videos—0
Learnable Cost Volume Using the Cayley RepresentationCode0
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation—0
Two-stream Fusion Model for Dynamic Hand Gesture Recognition using 3D-CNN and 2D-CNN Optical Flow guided Motion Template—0
SAILenv: Learning in Virtual Visual Environments Made SimpleCode0
Leveraging Category Information for Single-Frame Visual Sound Source SeparationCode0
CloudCast: A Satellite-Based Dataset and Baseline for Forecasting CloudsCode0
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos—0
Distributed optimization for nonrigid nano-tomographyCode0
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer—0
Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis—0
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer—0
Learning Model-Blind Temporal Denoisers without Ground Truths—0
Low-light Environment Neural SurveillanceCode0
FlowControl: Optical Flow Based Visual Servoing—0
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning—0
A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing—0
Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data—0
Movement Tracking by Optical Flow Assisted Inertial Navigation—0
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching—0
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network—0
Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision—0
Self-Supervised Representation Learning for Visual Anomaly Detection—0
End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention—0
Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN—0
Learning Fused Pixel and Feature-Based View Reconstructions for Light Fields—0
Learning Video Stabilization Using Optical Flow—0
An End-to-End Edge Aggregation Network for Moving Object Segmentation—0
Learning Nanoscale Motion Patterns of Vesicles in Living Cells—0
Scale-Space Flow for End-to-End Optimized Video Compression—0
Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training—0
Optical Flow in the Dark—0
3D human pose estimation with adaptive receptive fields and dilated temporal convolutions—0
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones—0
Spoof Face Detection Via Semi-Supervised Adversarial Training—0
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation—0
Self-supervised Transfer Learning for Instance Segmentation through Physical InteractionCode0
Analytic Signal Phase in N-D by Linear Symmetry Tensor--fingerprint modeling—0
Mean Oriented Riesz Features for Micro Expression Classification—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