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 151–175 of 212 papers

TitleStatusHype
Motion Segmentation Using Locally Affine Atom Voting—0
Motion Segmentation via Global and Local Sparse Subspace Optimization—0
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation—0
Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network—0
NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction—0
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation—0
Object Discovery in Videos as Foreground Motion Clustering—0
On Moving Object Segmentation from Monocular Video with Transformers—0
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes—0
PAMOCAT: Automatic retrieval of specified postures—0
Parametric Object Motion from Blur—0
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation—0
Quantum Motion Segmentation—0
Quickest Moving Object Detection—0
Reconstructing Articulated Rigged Models from RGB-D Videos—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Retina-Inspired Object Motion Segmentation for Event-Cameras—0
Rigid Motion Segmentation using Randomized Voting—0
Robust Multi-body Feature Tracker: A Segmentation-free Approach—0
Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model—0
Robust Subspace Segmentation with Block-diagonal Prior—0
Robust Video Background Identification by Dominant Rigid Motion Estimation—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
Scalable Sparse Subspace Clustering—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