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 1501–1550 of 2184 papers

TitleStatusHype
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow—0
PIV-Based 3D Fluid Flow Reconstruction Using Light Field Camera—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Pixel-level Correspondence for Self-Supervised Learning from Video—0
Pixel-wise Deep Image Stitching—0
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness—0
Playing for Benchmarks—0
Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features—0
PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow—0
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection—0
Point-wise mutual information-based video segmentation with high temporal consistency—0
Polarized Optical-Flow Gyroscope—0
PooDLe: Pooled and dense self-supervised learning from naturalistic videos—0
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks—0
Pose-based Deep Gait Recognition—0
PoseFlow: A Deep Motion Representation for Understanding Human Behaviors in Videos—0
Pose from Action: Unsupervised Learning of Pose Features based on Motion—0
Pose-Guided Fine-Grained Sign Language Video Generation—0
Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow—0
Post-Training Quantization for Video Matting—0
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising—0
PRAFlow_RVC: Pyramid Recurrent All-Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020—0
Precipitation Nowcasting with Satellite Imagery—0
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding—0
VideoCapsuleNet: A Simplified Network for Action Detection—0
Predicting ice flow using machine learning—0
Predicting Scene Parsing and Motion Dynamics in the Future—0
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor—0
Predictive Coding Networks Meet Action Recognition—0
Predictive-Corrective Networks for Action Detection—0
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models—0
A Video-Based Method for Objectively Rating Ataxia—0
Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention—0
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models—0
Probabilistic Future Prediction for Video Scene Understanding—0
A Video-Based Activity Classification of Human Pickers in Agriculture—0
Precipitation Downscaling with Spatiotemporal Video Diffusion—0
ProbFlow: Joint Optical Flow and Uncertainty Estimation—0
ProFlow: Learning to Predict Optical Flow—0
Progressive Deep Video Dehazing without Explicit Alignment Estimation—0
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation—0
Progressive Sparse Local Attention for Video object detection—0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
Projecting Trackable Thermal Patterns for Dynamic Computer Vision—0
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition—0
AutoScaler: Scale-Attention Networks for Visual Correspondence—0
Prototypical Transformer as Unified Motion Learners—0
ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking—0
Proximal Deep Structured Models—0
Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision—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