SOTAVerified

Motion Segmentation

Motion Segmentation is an essential task in many applications in Computer Vision and Robotics, such as surveillance, action recognition and scene understanding. The classic way to state the problem is the following: given a set of feature points that are tracked through a sequence of images, the goal is to cluster those trajectories according to the different motions they belong to. It is assumed that the scene contains multiple objects that are moving rigidly and independently in 3D-space.

Source: Robust Motion Segmentation from Pairwise Matches

Papers

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Showing 51–100 of 212 papers

TitleStatusHype
On Matrix Factorizations in Subspace ClusteringCode0
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow EstimationCode0
On the Usage of the Trifocal Tensor in Motion SegmentationCode0
Multi-Class Model Fitting by Energy Minimization and Mode-SeekingCode0
Moving Objects Detection with a Moving Camera: A Comprehensive ReviewCode0
A Multi-Scale Recurrent Framework for Motion Segmentation With Event CameraCode0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
Graph Constrained Data Representation Learning for Human Motion SegmentationCode0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
Motion Segmentation by Exploiting Complementary Geometric ModelsCode0
CV-MOS: A Cross-View Model for Motion SegmentationCode0
Motion-based Object Segmentation based on Dense RGB-D Scene FlowCode0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras—0
Context-Aware Modeling and Recognition of Activities in Video—0
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI—0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms—0
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering—0
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
A Detailed Rubric for Motion Segmentation—0
Event-based Egocentric Human Pose Estimation in Dynamic Environment—0
Consistency and Diversity induced Human Motion Segmentation—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos—0
Community Recovery in Hypergraphs—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
Dynamic Body VSLAM with Semantic Constraints—0
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations—0
Appearance-Based Refinement for Object-Centric Motion Segmentation—0
A Continuous Occlusion Model for Road Scene Understanding—0
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
Distributed Low-rank Subspace Segmentation—0
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices—0
Large Displacement Optical Flow from Nearest Neighbor Fields—0
Joint Motion Segmentation and Background Estimation in Dynamic Scenes—0
Learned Trajectory Embedding for Subspace Clustering—0
Jointly learning visual motion and confidence from local patches in event cameras—0
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding—0
Learning Motion Patterns in Videos—0
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces—0
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All datasetsApolloScapeHopkins155KT3DMoSegMTPV62

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rule BasedAccuracy90—Unverified
2Rel-Att-GCNAccuracy89—Unverified
3MRGCNAccuracy86—Unverified
4MRGCN-LSTMAccuracy72—Unverified
5St-RNNAccuracy63—Unverified
#ModelMetricClaimedVerifiedStatus
1SSCClassification Error2.18—Unverified
2T-LinkageClassification Error1.97—Unverified
3RSIMClassification Error1.01—Unverified
4MVCClassification Error0.31—Unverified
#ModelMetricClaimedVerifiedStatus
1MultiViewClusteringError7.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MVCClassification Error0.65—Unverified