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 171180 of 212 papers

TitleStatusHype
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces0
Robust Multi-body Feature Tracker: A Segmentation-free Approach0
Temporal Subspace Clustering for Human Motion Segmentation0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment0
Video Segmentation With Just a Few Strokes0
Differentially private subspace clustering0
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations0
Robust Subspace Clustering via Tighter Rank ApproximationCode0
Long-Range Trajectories from Global and Local Motion Representations0
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete DataCode0
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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