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 61–70 of 212 papers

TitleStatusHype
Monocular Arbitrary Moving Object Discovery and SegmentationCode1
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation—0
Event-based Motion Segmentation by Cascaded Two-Level Multi-Model FittingCode1
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Unsupervised Object Learning via Common FateCode0
NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction—0
Graph Constrained Data Representation Learning for Human Motion SegmentationCode0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving—0
On Matrix Factorizations in Subspace ClusteringCode0
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure—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