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 901–950 of 2184 papers

TitleStatusHype
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation—0
HashEncoding: Autoencoding with Multiscale Coordinate Hashing—0
A Multi-Modal Transformer Network for Action Detection—0
HDRVideo-GAN: Deep Generative HDR Video Reconstruction—0
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction—0
Group Activity Recognition using Unreliable Tracked Pose—0
Deep Transport Network for Unsupervised Video Object Segmentation—0
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Graph Neural Network Combining Event Stream and Periodic Aggregation for Low-Latency Event-based Vision—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching—0
Deep Temporal Interpolation of Radar-based Precipitation—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation—0
Hierarchical interaction network for video object segmentation from referring expressions—0
Hierarchically-Constrained Optical Flow—0
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences—0
Adaptive Intermediate Representations for Video Understanding—0
Google Map Aided Visual Navigation for UAVs in GPS-denied Environment—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
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video—0
Global Temporal Representation based CNNs for Infrared Action Recognition—0
Amora: Black-box Adversarial Morphing Attack—0
Global Motion Understanding in Large-Scale Video Object Segmentation—0
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction—0
DeepRM: Deep Recurrent Matching for 6D Pose Refinement—0
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects—0
How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study—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
GG-SSMs: Graph-Generating State Space Models—0
Human Action Recognition System using Good Features and Multilayer Perceptron Network—0
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines—0
Human Following for Wheeled Robot with Monocular Pan-tilt Camera—0
HuPerFlow: A Comprehensive Benchmark for Human vs. Machine Motion Estimation Comparison—0
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture—0
GFlow: Recovering 4D World from Monocular Video—0
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning—0
Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph—0
Hybrid Local-Global Context Learning for Neural Video Compression—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation—0
Amodal Optical Flow—0
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints—0
GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping—0
Deep Neural Networks in Video Human Action Recognition: A Review—0
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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