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 51–100 of 212 papers

TitleStatusHype
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces—0
Event-based Egocentric Human Pose Estimation in Dynamic Environment—0
A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems—0
Distributed Low-rank Subspace Segmentation—0
Joint Motion Segmentation and Background Estimation in Dynamic Scenes—0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms—0
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI—0
Jointly learning visual motion and confidence from local patches in event cameras—0
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces—0
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos—0
Clustering with Hypergraphs: The Case for Large Hyperedges—0
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization—0
Differentially private subspace clustering—0
DGSAC: Density Guided Sampling and Consensus—0
Channel-wise Motion Features for Efficient Motion Segmentation—0
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery—0
Learning Video Object Segmentation with Visual Memory—0
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation—0
Deep Learning for Robust Motion Segmentation with Non-Static Cameras—0
Image Segmentation Using Subspace Representation and Sparse Decomposition—0
Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing—0
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera—0
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline—0
Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation—0
Is an Affine Constraint Needed for Affine Subspace Clustering?—0
It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos—0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition—0
Greedy Subspace Clustering—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices—0
Large Displacement Optical Flow from Nearest Neighbor Fields—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
Learned Trajectory Embedding for Subspace Clustering—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding—0
Learning Motion Patterns in Videos—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation—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