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 3140 of 212 papers

TitleStatusHype
Video Class Agnostic Segmentation Benchmark for Autonomous DrivingCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Moving Objects Detection with a Moving Camera: A Comprehensive ReviewCode0
Motion Segmentation by Exploiting Complementary Geometric ModelsCode0
Multi-Class Model Fitting by Energy Minimization and Mode-SeekingCode0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Adaptive Low-Rank Kernel Subspace ClusteringCode0
Motion-based Object Segmentation based on Dense RGB-D Scene FlowCode0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
KDMOS:Knowledge Distillation for Motion SegmentationCode0
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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