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
Event-based Motion Segmentation with Spatio-Temporal Graph CutsCode1
Learning to Segment Rigid Motions from Two FramesCode1
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment0
Context-Aware Modeling and Recognition of Activities in Video0
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI0
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering0
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation0
A Detailed Rubric for Motion Segmentation0
Consistency and Diversity induced Human Motion Segmentation0
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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