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 191200 of 212 papers

TitleStatusHype
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices0
Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery0
Greedy Subspace Clustering0
Robust Subspace Segmentation with Block-diagonal Prior0
Video Motion Segmentation Using New Adaptive Manifold Denoising Model0
Segmentation-Free Dynamic Scene Deblurring0
Joint Motion Segmentation and Background Estimation in Dynamic Scenes0
Rigid Motion Segmentation using Randomized Voting0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images0
Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing0
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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