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 71–80 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 Segmentation—0
SpikeMS: Deep Spiking Neural Network for Motion Segmentation—0
Local Frequency Domain Transformer Networks for Video PredictionCode1
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery—0
Spherical formulation of geometric motion segmentation constraints in fisheye cameras—0
Self-supervised Video Object Segmentation by Motion Grouping—0
Video Class Agnostic Segmentation Benchmark for Autonomous DrivingCode1
Deep Learning for Robust Motion Segmentation with Non-Static Cameras—0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
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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