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 551–600 of 2184 papers

TitleStatusHype
Beyond Brightness Constancy: Learning Noise Models for Optical Flow—0
Adaptive Semantic-Spatio-Temporal Graph Convolutional Network for Lip Reading—0
Exploring More from Multiple Gait Modalities for Human Identification—0
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation—0
DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba—0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Exploiting Semantic Information and Deep Matching for Optical Flow—0
DeGraF-Flow: Extending DeGraF Features for accurate and efficient sparse-to-dense optical flow estimation—0
Deformation Monitoring of Tunnel using Phase-based Motion Magnification and Optical Flow—0
Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos—0
Improve Myocardial Strain Estimation based on Deformable Groupwise Registration with a Locally Low-Rank Dissimilarity Metric—0
Best of Both Worlds: Hybrid SNN-ANN Architecture for Event-based Optical Flow Estimation—0
Analysis of Latent-Space Motion for Collaborative Intelligence—0
A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM—0
DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints—0
Adaptive mixed norm optical flow estimation—0
A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection—0
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation—0
Exploiting Inductive Biases in Video Modeling through Neural CDEs—0
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding—0
Face Flow—0
Deep Video Codec Control for Vision Models—0
A Multi-Modal Transformer Network for Action Detection—0
3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints—0
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation—0
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation—0
Deep Transport Network for Unsupervised Video Object Segmentation—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Deep Temporal Interpolation of Radar-based Precipitation—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
Adaptive Intermediate Representations for Video Understanding—0
Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves—0
Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation—0
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition—0
Amora: Black-box Adversarial Morphing Attack—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
DeepRM: Deep Recurrent Matching for 6D Pose Refinement—0
Deep Predictive Video Compression with Bi-directional Prediction—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Amodal Optical Flow—0
Evolving Losses for Unlabeled Video Representation Learning—0
Deep Neural Networks in Video Human Action Recognition: A Review—0
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness—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