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
Motion Segmentation using Frequency Domain Transformer NetworksCode1
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI—0
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation—0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition—0
Moving Objects Detection with a Moving Camera: A Comprehensive ReviewCode0
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering—0
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution—0
Subspace Clustering with Active Learning—0
Stereo-based Multi-motion Visual Odometry for Mobile Robots—0
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding—0
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion SaliencyCode0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model—0
Motion Segmentation Using Locally Affine Atom Voting—0
Progressive-X: Efficient, Anytime, Multi-Model Fitting AlgorithmCode1
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching VideosCode1
Robust Motion Segmentation from Pairwise MatchesCode0
Event-Based Motion Segmentation by Motion CompensationCode1
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras—0
Robust Video Background Identification by Dominant Rigid Motion Estimation—0
Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks—0
Towards Segmenting Anything That MovesCode0
Using Motion and Internal Supervision in Object Recognition—0
Object Discovery in Videos as Foreground Motion Clustering—0
Extending Layered Models to 3D MotionCode0
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow EstimationCode0
FlowNet3D: Learning Scene Flow in 3D Point CloudsCode1
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation—0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Motion-based Object Segmentation based on Dense RGB-D Scene FlowCode0
Image Segmentation Using Subspace Representation and Sparse Decomposition—0
Motion Segmentation by Exploiting Complementary Geometric ModelsCode0
Fast Piecewise-Affine Motion Estimation Without Segmentation—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Learning to Segment Moving Objects—0
CUR Decompositions, Similarity Matrices, and Subspace Clustering—0
Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos—0
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving—0
Community Recovery in Hypergraphs—0
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models—0
Adaptive Low-Rank Kernel Subspace ClusteringCode0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces—0
Multi-Class Model Fitting by Energy Minimization and Mode-SeekingCode0
Learning Video Object Segmentation with Visual Memory—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation—0
Motion Segmentation via Global and Local Sparse Subspace Optimization—0
Learning Motion Patterns in Videos—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