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

Showing 15511600 of 2184 papers

TitleStatusHype
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation0
Unsupervised Segmentation of Fire and Smoke from Infra-Red Videos0
Global Temporal Representation based CNNs for Infrared Action Recognition0
Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D ModuleCode0
An Internal Learning Approach to Video InpaintingCode0
Motion Guided Attention for Video Salient Object DetectionCode0
Temporally Consistent Depth Prediction with Flow-Guided Memory Units0
Learning Residual Flow as Dynamic Motion from Stereo Videos0
Visuomotor Understanding for Representation Learning of Driving Scenes0
Multimodal Deep Models for Predicting Affective Responses Evoked by MoviesCode0
Human Following for Wheeled Robot with Monocular Pan-tilt Camera0
Flow-Motion and Depth Network for Monocular Stereo and BeyondCode0
Learning Task-Specific Generalized Convolutions in the Permutohedral LatticeCode0
Video Interpolation and Prediction with Unsupervised Landmarks0
Multiple Object Tracking with Motion and Appearance Cues0
Parallel Unbalanced Optimal Transport Regularization for Large Scale Imaging Problems0
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation0
Multi-Stream Single Shot Spatial-Temporal Action Detection0
Video-based Bottleneck Detection utilizing Lagrangian Dynamics in Crowded Scenes0
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition0
In defense of OSVOS0
Some Aspects of Geometric Computer Vision for Analysing Dynamical Scenes focusing Automotive Applications0
Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous Vehicles0
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation0
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks0
Mono-Stixels: Monocular depth reconstruction of dynamic street scenes0
Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow0
Differential Scene Flow from Light Field Gradients0
Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTMCode0
StableNet: Semi-Online, Multi-Scale Deep Video Stabilization0
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach0
Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information0
Scene Motion Decomposition for Learnable Visual Odometry0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera0
Spacetime Graph Optimization for Video Object Segmentation0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue0
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative TrainingCode0
Robustness Guarantees for Deep Neural Networks on Videos0
DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints0
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs0
Evolving Losses for Unlabeled Video Representation Learning0
Video Modeling with Correlation Networks0
Learning Temporal Pose Estimation from Sparsely-Labeled VideosCode0
Two-Stream Region Convolutional 3D Network for Temporal Activity Detection0
PA3D: Pose-Action 3D Machine for Video Recognition0
Creative Flow+ DatasetCode0
Robust Video Stabilization by Optimization in CNN Weight Space0
MARS: Motion-Augmented RGB Stream for Action RecognitionCode0
Event Cameras, Contrast Maximization and Reward Functions: An AnalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64Unverified
2FastFlowNet-ftAverage End-Point Error4.89Unverified
3UnrolledCostAverage End-Point Error4.69Unverified
4LiteFlowNet-ftAverage End-Point Error4.54Unverified
5FlowNet2Average End-Point Error3.96Unverified
6IRR-PWCAverage End-Point Error3.84Unverified
7SelFlowAverage End-Point Error3.74Unverified
8FDFlowNet-ftAverage End-Point Error3.71Unverified
9ScopeFlowAverage End-Point Error3.59Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36Unverified
2FastFlowNet-ftAverage End-Point Error6.08Unverified
3UnrolledCostAverage End-Point Error5.8Unverified
4MR-FlowAverage End-Point Error5.38Unverified
5LiteFlowNet-ftAverage End-Point Error5.38Unverified
6FDFlowNet-ftAverage End-Point Error5.11Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69Unverified
8IRR-PWCAverage End-Point Error4.58Unverified
9LiteFlowNet3-SAverage End-Point Error4.53Unverified
10ContinualFlow + ftAverage End-Point Error4.52Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7Unverified
2FastFlowNetF1-all33.1Unverified
3FlowNet2F1-all30Unverified
4VCNF1-all25.1Unverified
5HD3F1-all24Unverified
6MaskFlowNetF1-all23.1Unverified
7SCVF1-all19.3Unverified
8RAPIDFlowF1-all17.7Unverified
9CRAFTF1-all17.5Unverified
10RAFTF1-all17.4Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22Unverified
2UnrolledCostFl-all10.81Unverified
3LiteFlowNet-ftFl-all9.38Unverified
4SelFlowFl-all8.42Unverified
5IRR-PWCFl-all7.65Unverified
6LiteFlowNet2-ftFl-all7.62Unverified
7LiteFlowNet3Fl-all7.34Unverified
8LiteFlowNet3-SFl-all7.22Unverified
9MaskFlownet-SFl-all6.81Unverified
10RAPIDFlowFl-all6.12Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8Unverified
2LiteFlowNet-ftAverage End-Point Error1.6Unverified
3IRR-PWCAverage End-Point Error1.6Unverified
4SelFlowAverage End-Point Error1.5Unverified
5FDFlowNet-ftAverage End-Point Error1.5Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4Unverified
8LiteFlowNet3-SAverage End-Point Error1.3Unverified
9LiteFlowNet3Average End-Point Error1.3Unverified
10MaskFlownetAverage End-Point Error1.1Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27Unverified
2SPyNet1px total29.96Unverified
3GMFlow1px total10.36Unverified
4GMA1px total7.07Unverified
5RAFT1px total6.79Unverified
6FlowNet21px total6.71Unverified
7FlowFormer1px total6.51Unverified
8MS-RAFT+1px total5.72Unverified
9RPKNet1px total4.81Unverified
10DPFlow1px total3.44Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21Unverified
2MDFlow-FastAverage End-Point Error4.73Unverified
3UpFlowAverage End-Point Error4.68Unverified
4ARFlow-MVAverage End-Point Error4.49Unverified
5MDFlowAverage End-Point Error4.16Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5Unverified
2MDFlow-FastAverage End-Point Error5.99Unverified
3ARFlow-MVAverage End-Point Error5.67Unverified
4MDFlowAverage End-Point Error5.46Unverified
5UpFlowAverage End-Point Error5.32Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79Unverified
2MDFlow-FastFl-all11.43Unverified
3UpFlowFl-all9.38Unverified
4MDFlowFl-all8.91Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5Unverified
2UpFlowAverage End-Point Error1.4Unverified