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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 212 papers

TitleStatusHype
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation—0
Deep Learning for Robust Motion Segmentation with Non-Static Cameras—0
Image Segmentation Using Subspace Representation and Sparse Decomposition—0
Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing—0
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera—0
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline—0
Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation—0
Is an Affine Constraint Needed for Affine Subspace Clustering?—0
It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos—0
Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition—0
Greedy Subspace Clustering—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices—0
Large Displacement Optical Flow from Nearest Neighbor Fields—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
Learned Trajectory Embedding for Subspace Clustering—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding—0
Learning Motion Patterns in Videos—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object 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