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 101–150 of 212 papers

TitleStatusHype
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
Scene Flow from Point Clouds with or without Learning—0
Segmentation-Free Dynamic Scene Deblurring—0
Segmentation Guided Deep HDR Deghosting—0
Self-Supervised Deep Subspace Clustering with Entropy-norm—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Self-supervised Sparse to Dense Motion Segmentation—0
Self-supervised Video Object Segmentation by Motion Grouping—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery—0
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation—0
Spherical formulation of geometric motion segmentation constraints in fisheye cameras—0
SpikeMS: Deep Spiking Neural Network for Motion Segmentation—0
Stereo-based Multi-motion Visual Odometry for Mobile Robots—0
Structured Sparse Subspace Clustering: A Unified Optimization Framework—0
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework—0
Subspace Clustering with Active Learning—0
Temporally Consistent Motion Segmentation from RGB-D Video—0
Temporal Rate Reduction Clustering for Human Motion Segmentation—0
Temporal Subspace Clustering for Human Motion Segmentation—0
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution—0
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation—0
The Ordered Residual Kernel for Robust Motion Subspace Clustering—0
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields—0
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence—0
Unbiased Sparse Subspace Clustering By Selective Pursuit—0
Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation—0
Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation—0
Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information—0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes—0
Segmenting the motion components of a video: A long-term unsupervised model—0
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure—0
Using Motion and Internal Supervision in Object Recognition—0
Video Motion Segmentation Using New Adaptive Manifold Denoising Model—0
Video Segmentation With Just a Few Strokes—0
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process—0
WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge—0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
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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