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

Showing 101110 of 212 papers

TitleStatusHype
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline0
Jointly learning visual motion and confidence from local patches in event cameras0
On the Usage of the Trifocal Tensor in Motion SegmentationCode0
DGSAC: Density Guided Sampling and Consensus0
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation0
Learning Visual Motion Segmentation Using Event Surfaces0
Is an Affine Constraint Needed for Affine Subspace Clustering?0
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI0
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rule BasedAccuracy90Unverified
2Rel-Att-GCNAccuracy89Unverified
3MRGCNAccuracy86Unverified
4MRGCN-LSTMAccuracy72Unverified
5St-RNNAccuracy63Unverified
#ModelMetricClaimedVerifiedStatus
1SSCClassification Error2.18Unverified
2T-LinkageClassification Error1.97Unverified
3RSIMClassification Error1.01Unverified
4MVCClassification Error0.31Unverified
#ModelMetricClaimedVerifiedStatus
1MultiViewClusteringError7.92Unverified
#ModelMetricClaimedVerifiedStatus
1MVCClassification Error0.65Unverified