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 7180 of 212 papers

TitleStatusHype
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation0
SpikeMS: Deep Spiking Neural Network for Motion Segmentation0
Local Frequency Domain Transformer Networks for Video PredictionCode1
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery0
Spherical formulation of geometric motion segmentation constraints in fisheye cameras0
Self-supervised Video Object Segmentation by Motion Grouping0
Video Class Agnostic Segmentation Benchmark for Autonomous DrivingCode1
Deep Learning for Robust Motion Segmentation with Non-Static Cameras0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
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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