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 1801–1850 of 2184 papers

TitleStatusHype
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum—0
Spacetime Graph Optimization for Video Object Segmentation—0
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow—0
Sparse data interpolation using the geodesic distance affinity space—0
Sparse Optical Flow-Based Line Feature Tracking—0
Sparsity Based Methods for Overparameterized Variational Problems—0
Spatial Distribution Patterns of Clownfish in Recirculating Aquaculture Systems—0
Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation—0
Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition—0
Spatio-Temporal-based Context Fusion for Video Anomaly Detection—0
Analytic Signal Phase in N-D by Linear Symmetry Tensor--fingerprint modeling—0
Spatio-Temporal Context Modeling for Road Obstacle Detection—0
Video-Story Composition via Plot Analysis—0
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields—0
3D-FlowNet: Event-based optical flow estimation with 3D representation—0
Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition—0
Violence detection in videos using deep recurrent and convolutional neural networks—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Spatio-Temporal Relation Learning for Video Anomaly Detection—0
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction—0
Analysis of Latent-Space Motion for Collaborative Intelligence—0
Spatio-temporal Transformer Network for Video Restoration—0
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models—0
A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM—0
SpeDo: 6 DOF Ego-Motion Sensor Using Speckle Defocus Imaging—0
Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information—0
A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection—0
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System—0
A Multi-Modal Transformer Network for Action Detection—0
Splatter a Video: Video Gaussian Representation for Versatile Processing—0
Splatting-based Synthesis for Video Frame Interpolation—0
SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs—0
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain—0
Spontaneous vs. Posed smiles - can we tell the difference?—0
Spoof Face Detection Via Semi-Supervised Adversarial Training—0
SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting—0
ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization—0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation—0
Amora: Black-box Adversarial Morphing Attack—0
Amodal Optical Flow—0
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera—0
Virtual Piano using Computer Vision—0
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising—0
StableNet: Semi-Online, Multi-Scale Deep Video Stabilization—0
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos—0
A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis—0
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—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