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 15011550 of 2184 papers

TitleStatusHype
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration0
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos0
Training Deep SLAM on Single Frames0
Deep motion estimation for parallel inter-frame prediction in video compressionCode0
Machine Learning for Precipitation Nowcasting from Radar Images0
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition0
Self-supervised Object Motion and Depth Estimation from Video0
Amora: Black-box Adversarial Morphing Attack0
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution0
Kernel learning for visual perceptionCode0
15 Keypoints Is All You Need0
Audio-Visual Target Speaker Enhancement on Multi-Talker Environment using Event-Driven Cameras0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations0
EventGAN: Leveraging Large Scale Image Datasets for Event CamerasCode0
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving0
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow0
Learning End-To-End Scene Flow by Distilling Single Tasks KnowledgeCode0
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion RecognitionCode0
Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation0
End to end collision avoidance based on optical flow and neural networks0
Deep Flow Collaborative Network for Online Visual Tracking0
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks0
Virtual Piano using Computer Vision0
SENSE: a Shared Encoder Network for Scene-flow EstimationCode0
Learning Multi-Human Optical FlowCode0
Anchor Diffusion for Unsupervised Video Object SegmentationCode0
Attacking Optical FlowCode0
Predictive Coding Networks Meet Action Recognition0
Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments0
Predicting ice flow using machine learning0
Video Person Re-Identification using Learned Clip Similarity Aggregation0
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light0
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks0
OmniTrack: Real-time detection and tracking of objects, text and logos in video0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models0
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving0
Compressed Video Action Recognition with Refined Motion Vector0
Object Segmentation Tracking from Generic Video Cues0
Occlusion-Aware Networks for 3D Human Pose Estimation in Video0
Neural Inter-Frame Compression for Video Coding0
RainFlow: Optical Flow Under Rain Streaks and Rain Veiling Effect0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos0
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations0
Multi-View Image Fusion0
Track to Reconstruct and Reconstruct to TrackCode0
CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule RoutingCode0
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion SaliencyCode0
Synthetic Data for Deep Learning0
Deformable Non-local Network for Video Super-ResolutionCode0
Show:102550
← PrevPage 31 of 44Next →

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