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
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks—0
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost VolumeCode1
Abnormal Event Detection in Videos using Generative Adversarial Nets—0
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
Human Action Recognition System using Good Features and Multilayer Perceptron Network—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
ProbFlow: Joint Optical Flow and Uncertainty Estimation—0
MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation—0
Segmentation-Aware Convolutional Networks Using Local Attention MasksCode0
Lattice Long Short-Term Memory for Human Action Recognition—0
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel—0
Semantic Video CNNs through Representation WarpingCode0
Video Frame Interpolation via Adaptive Separable ConvolutionCode0
Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii—0
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality—0
Cascaded Scene Flow Prediction using Semantic Segmentation—0
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning—0
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow EstimationCode0
Scalable Full Flow with Learned Binary Descriptors—0
DenseNet for Dense FlowCode0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
End-to-End Learning of Video Super-Resolution with Motion Compensation—0
Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data—0
Video Desnowing and Deraining Based on Matrix Decomposition—0
Deep Crisp Boundaries—0
Filter Flow Made Practical: Massively Parallel and Lock-FreeCode0
Online Video Object Segmentation via Convolutional Trident Network—0
Budget-Aware Deep Semantic Video Segmentation—0
S2F: Slow-To-Fast Interpolator Flow—0
Direct Photometric Alignment by Mesh Deformation—0
Robust Interpolation of Correspondences for Large Displacement Optical FlowCode0
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks—0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging—0
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation—0
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition—0
Deep Learning for Precipitation Nowcasting: A Benchmark and A New ModelCode0
Unsupervised learning of object frames by dense equivariant image labelling—0
Deep Frame Interpolation—0
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation—0
Towards Visual Ego-motion Learning in Robots—0
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks—0
Learning Image Relations with Contrast Association Networks—0
Single Image Action Recognition by Predicting Space-Time Saliency—0
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural NetworksCode1
Motion Prediction Under Multimodality with Conditional Stochastic Networks—0
Characterizing and Improving Stability in Neural Style Transfer—0
Optical Flow in Mostly Rigid Scenes—0
SfM-Net: Learning of Structure and Motion from Video—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