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 161–170 of 212 papers

TitleStatusHype
Parametric Object Motion from Blur—0
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation—0
Quantum Motion Segmentation—0
Quickest Moving Object Detection—0
Reconstructing Articulated Rigged Models from RGB-D Videos—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Retina-Inspired Object Motion Segmentation for Event-Cameras—0
Rigid Motion Segmentation using Randomized Voting—0
Robust Multi-body Feature Tracker: A Segmentation-free Approach—0
Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rule BasedAccuracy90—Unverified
2Rel-Att-GCNAccuracy89—Unverified
3MRGCNAccuracy86—Unverified
4MRGCN-LSTMAccuracy72—Unverified
5St-RNNAccuracy63—Unverified
#ModelMetricClaimedVerifiedStatus
1SSCClassification Error2.18—Unverified
2T-LinkageClassification Error1.97—Unverified
3RSIMClassification Error1.01—Unverified
4MVCClassification Error0.31—Unverified
#ModelMetricClaimedVerifiedStatus
1MultiViewClusteringError7.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MVCClassification Error0.65—Unverified