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 1120 of 212 papers

TitleStatusHype
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical FlowCode1
MOD-UV: Learning Mobile Object Detectors from Unlabeled VideosCode1
Local Frequency Domain Transformer Networks for Video PredictionCode1
0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event CameraCode1
EM-driven unsupervised learning for efficient motion segmentationCode1
Event-based Motion Segmentation by Cascaded Two-Level Multi-Model FittingCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Event-based Motion Segmentation with Spatio-Temporal Graph CutsCode1
Discovering Objects that Can MoveCode1
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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