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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 212 papers

TitleStatusHype
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI—0
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation—0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation—0
Channel-wise Motion Features for Efficient Motion Segmentation—0
Clustering with Hypergraphs: The Case for Large Hyperedges—0
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces—0
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces—0
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations—0
Community Recovery in Hypergraphs—0
Consistency and Diversity induced Human Motion Segmentation—0
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering—0
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering—0
Context-Aware Modeling and Recognition of Activities in Video—0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
Convex Relaxation of Mixture Regression with Efficient Algorithms—0
CUR Decompositions, Similarity Matrices, and Subspace Clustering—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Deep Learning for Robust Motion Segmentation with Non-Static Cameras—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
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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