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 151–200 of 212 papers

TitleStatusHype
Learning Motion Patterns in Videos—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
A Detailed Rubric for Motion Segmentation—0
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework—0
Clustering with Hypergraphs: The Case for Large Hyperedges—0
Unbiased Sparse Subspace Clustering By Selective Pursuit—0
Reconstructing Articulated Rigged Models from RGB-D Videos—0
Temporally Consistent Motion Segmentation from RGB-D Video—0
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation—0
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera—0
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects—0
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process—0
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Motion From Structure (MfS): Searching for 3D Objects in Cluttered Point Trajectories—0
A Continuous Occlusion Model for Road Scene Understanding—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Achieving stable subspace clustering by post-processing generic clustering results—0
Quickest Moving Object Detection—0
Parametric Object Motion from Blur—0
It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos—0
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces—0
Robust Multi-body Feature Tracker: A Segmentation-free Approach—0
Video Segmentation With Just a Few Strokes—0
Differentially private subspace clustering—0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
Temporal Subspace Clustering for Human Motion Segmentation—0
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations—0
Robust Subspace Clustering via Tighter Rank ApproximationCode0
Long-Range Trajectories from Global and Local Motion Representations—0
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete DataCode0
Robust Subspace Clustering via Smoothed Rank ApproximationCode0
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification—0
LogDet Rank Minimization with Application to Subspace Clustering—0
Structured Sparse Subspace Clustering: A Unified Optimization Framework—0
Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Learning Similarity Metrics for Dynamic Scene Segmentation—0
Dynamic Body VSLAM with Semantic Constraints—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
Learning Articulated Motions From Visual DemonstrationCode0
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices—0
Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery—0
Greedy Subspace Clustering—0
Joint Motion Segmentation and Background Estimation in Dynamic Scenes—0
Segmentation-Free Dynamic Scene Deblurring—0
Robust Subspace Segmentation with Block-diagonal Prior—0
Rigid Motion Segmentation using Randomized Voting—0
Video Motion Segmentation Using New Adaptive Manifold Denoising Model—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—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