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 1051–1100 of 2184 papers

TitleStatusHype
RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical FlowCode0
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion—0
Spatio-Temporal Relation Learning for Video Anomaly Detection—0
Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition—0
Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics—0
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images using Images, Normals and Vertices—0
NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter—0
EDeNN: Event Decay Neural Networks for low latency vision—0
Temporal Flow Mask Attention for Open-Set Long-Tailed Recognition of Wild Animals in Camera-Trap Images—0
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising—0
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained VideosCode0
A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation—0
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising—0
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data—0
Unsupervised Fish Trajectory Tracking and Segmentation—0
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow—0
Exploring Long- and Short-Range Temporal Information for Learned Video CompressionCode0
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation—0
Leveraging the HW/SW Optimizations and Ecosystems that Drive the AI Revolution—0
Unsupervised Flow Refinement near Motion Boundaries—0
Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily LivingCode0
Dyadic Movement Synchrony Estimation Under Privacy-preserving Conditions—0
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation—0
Motion-aware Memory Network for Fast Video Salient Object DetectionCode0
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation—0
A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features—0
Deep 360^ Optical Flow Estimation Based on Multi-Projection Fusion—0
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning—0
Key frames assisted hybrid encoding for photorealistic compressive video sensing—0
Error-Aware Spatial Ensembles for Video Frame Interpolation—0
Hybrid Classifiers for Spatio-temporal Real-time Abnormal Behaviors Detection, Tracking, and Recognition in Massive Hajj Crowds—0
Kinematics Modeling Network for Video-based Human Pose EstimationCode0
Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application—0
On an Edge-Preserving Variational Model for Optical Flow Estimation—0
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation LearningCode0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks—0
Weakly Supervised Video Salient Object Detection via Point Supervision—0
Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models—0
Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapyCode0
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated ContentCode0
Ego-motion Estimation Based on Fusion of Images and EventsCode0
Learned Video Compression via Heterogeneous Deformable Compensation Network—0
Complementing Brightness Constancy with Deep Networks for Optical Flow PredictionCode0
Pixel-level Correspondence for Self-Supervised Learning from Video—0
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera—0
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models—0
SC-Transformer++: Structured Context Transformer for Generic Event Boundary DetectionCode0
Motion Estimation for Large Displacements and Deformations—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