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 6170 of 212 papers

TitleStatusHype
Monocular Arbitrary Moving Object Discovery and SegmentationCode1
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation0
Event-based Motion Segmentation by Cascaded Two-Level Multi-Model FittingCode1
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation0
Unsupervised Object Learning via Common FateCode0
NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction0
Graph Constrained Data Representation Learning for Human Motion SegmentationCode0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving0
On Matrix Factorizations in Subspace ClusteringCode0
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure0
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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