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 181190 of 212 papers

TitleStatusHype
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking0
Deep Learning for Robust Motion Segmentation with Non-Static Cameras0
Dense Monocular Depth Estimation in Complex Dynamic Scenes0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach0
DGSAC: Density Guided Sampling and Consensus0
Differentially private subspace clustering0
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos0
Distributed Low-rank Subspace Segmentation0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots0
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation0
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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