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 131140 of 212 papers

TitleStatusHype
Temporal Rate Reduction Clustering for Human Motion Segmentation0
Temporal Subspace Clustering for Human Motion Segmentation0
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution0
The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation0
The Ordered Residual Kernel for Robust Motion Subspace Clustering0
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields0
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence0
Unbiased Sparse Subspace Clustering By Selective Pursuit0
Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation0
Un-EvMoSeg: Unsupervised Event-based Independent 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