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 1201–1250 of 2184 papers

TitleStatusHype
Enhanced Correlation Matching based Video Frame Interpolation—0
Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking—0
Consistent Semantic Attacks on Optical Flow—0
Self-Supervised Real-time Video Stabilization—0
Triple-level Model Inferred Collaborative Network Architecture for Video DerainingCode0
KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action LocalizationCode0
Efficient Neuromorphic Signal Processing with Loihi 2—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Joint Detection of Motion Boundaries and OcclusionsCode0
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation—0
HDRVideo-GAN: Deep Generative HDR Video Reconstruction—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
ReFIn: A Refinement Approach for Video Frame Interpolation—0
Temporally stable video segmentation without video annotations—0
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021—0
Talking Detection In Collaborative Learning Environments—0
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution—0
Quadrotor going through a window and landing: An image-based visual servo control approach—0
Learning Higher-Order Dynamics in Video-Based Cardiac Measurement—0
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos—0
Accelerated First Order Methods for Variational ImagingCode0
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer—0
A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation—0
TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs—0
LOF: Structure-Aware Line Tracking based on Optical Flow—0
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors—0
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural NetworkCode0
Automatic Portrait Video Matting via Context Motion Network—0
Temporally Coherent Person Matting Trained on Fake-Motion Dataset—0
SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos—0
A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data—0
Learning Cross-modal Contrastive Features for Video Domain Adaptation—0
Full-Velocity Radar Returns by Radar-Camera Fusion—0
MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition—0
Adaptive Semantic-Spatio-Temporal Graph Convolutional Network for Lip Reading—0
Deep Motion Prior for Weakly-Supervised Temporal Action Localization—0
An optical flow approach to tracking ship track behavior using GOES-R satellite imagery—0
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos—0
Feature-Supervised Action Modality Transfer—0
End-to-end Neural Video Coding Using a Compound Spatiotemporal Representation—0
Neural Image Representations for Multi-Image Fusion and Layer Separation—0
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition—0
Using Visual Anomaly Detection for Task Execution MonitoringCode0
Temporal Feature Warping for Video Shadow Detection—0
Detail Preserving Residual Feature Pyramid Modules for Optical Flow—0
Deep Iterative 2D/3D Registration—0
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance ImagingCode0
Progressive Deep Video Dehazing without Explicit Alignment Estimation—0
MeNToS: Tracklets Association with a Space-Time Memory Network—0
DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction—0
Show:102550
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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