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

Showing 5175 of 212 papers

TitleStatusHype
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete DataCode0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Moving Objects Detection with a Moving Camera: A Comprehensive ReviewCode0
Motion Segmentation by Exploiting Complementary Geometric ModelsCode0
A Multi-Scale Recurrent Framework for Motion Segmentation With Event CameraCode0
Motion-based Object Segmentation based on Dense RGB-D Scene FlowCode0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
CV-MOS: A Cross-View Model for Motion SegmentationCode0
Learning Articulated Motions From Visual DemonstrationCode0
KDMOS:Knowledge Distillation for Motion SegmentationCode0
Iterative Event-based Motion Segmentation by Variational Contrast MaximizationCode0
Multi-Class Model Fitting by Energy Minimization and Mode-SeekingCode0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras0
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras0
Context-Aware Modeling and Recognition of Activities in Video0
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms0
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering0
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation0
A Detailed Rubric for Motion Segmentation0
Event-based Egocentric Human Pose Estimation in Dynamic Environment0
Consistency and Diversity induced Human Motion Segmentation0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rule BasedAccuracy90Unverified
2Rel-Att-GCNAccuracy89Unverified
3MRGCNAccuracy86Unverified
4MRGCN-LSTMAccuracy72Unverified
5St-RNNAccuracy63Unverified
#ModelMetricClaimedVerifiedStatus
1SSCClassification Error2.18Unverified
2T-LinkageClassification Error1.97Unverified
3RSIMClassification Error1.01Unverified
4MVCClassification Error0.31Unverified
#ModelMetricClaimedVerifiedStatus
1MultiViewClusteringError7.92Unverified
#ModelMetricClaimedVerifiedStatus
1MVCClassification Error0.65Unverified