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 1901–1950 of 2184 papers

TitleStatusHype
Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games—0
Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow—0
3D Convolutional with Attention for Action Recognition—0
A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking—0
A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing—0
TDE-3: An improved prior for optical flow computation in spiking neural networks—0
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021—0
Technical Report on Visual Quality Assessment for Frame Interpolation—0
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation—0
Temporal-Consistent Video Restoration with Pre-trained Diffusion Models—0
Temporal Early Exits for Efficient Video Object Detection—0
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection—0
Temporal Feature Warping for Video Shadow Detection—0
Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images—0
Temporal Hockey Action Recognition via Pose and Optical Flows—0
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View—0
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation—0
Temporally coherent completion of dynamic video—0
Temporally Coherent Person Matting Trained on Fake-Motion Dataset—0
Temporally Consistent Depth Prediction with Flow-Guided Memory Units—0
Temporally stable video segmentation without video annotations—0
Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images—0
Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos—0
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution—0
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos—0
3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks—0
The Analysis of Facial Feature Deformation using Optical Flow Algorithm—0
The Aperture Problem for Refractive Motion—0
The Background Also Matters: Background-Aware Motion-Guided Objects Discovery—0
The benefits of synthetic data for action categorization—0
The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow—0
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation—0
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation—0
The Error is the Feature: how to Forecast Lightning using a Model Prediction Error—0
Improving Optical Flow on a Pyramid Level—0
The Global Patch Collider—0
The U-Net based GLOW for Optical-Flow-free Video Interframe Generation—0
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild—0
2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework—0
A Feature Clustering Approach Based on Histogram of Oriented Optical Flow and Superpixels—0
2D LiDAR Map Prediction via Estimating Motion Flow with GRU—0
A Fast and Accurate Optical Flow Camera for Resource-Constrained Edge Applications—0
Time-based Mapping of Space Using Visual Motion Invariants—0
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map—0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos—0
Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera—0
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion—0
TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation—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