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

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Showing 176–200 of 212 papers

TitleStatusHype
Context-Aware Modeling and Recognition of Activities in Video—0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
Convex Relaxation of Mixture Regression with Efficient Algorithms—0
CUR Decompositions, Similarity Matrices, and Subspace Clustering—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Deep Learning for Robust Motion Segmentation with Non-Static Cameras—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
DGSAC: Density Guided Sampling and Consensus—0
Differentially private subspace clustering—0
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos—0
Distributed Low-rank Subspace Segmentation—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation—0
Dynamic Body VSLAM with Semantic Constraints—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Event-based Egocentric Human Pose Estimation in Dynamic Environment—0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms—0
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
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All datasetsApolloScapeHopkins155KT3DMoSegMTPV62

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