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 1–10 of 212 papers

TitleStatusHype
Channel-wise Motion Features for Efficient Motion Segmentation—0
Temporal Rate Reduction Clustering for Human Motion Segmentation—0
KDMOS:Knowledge Distillation for Motion SegmentationCode0
FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian VelocityCode1
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Event-based Egocentric Human Pose Estimation in Dynamic Environment—0
Iterative Event-based Motion Segmentation by Variational Contrast MaximizationCode0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation—0
Wandering around: A bioinspired approach to visual attention through object motion sensitivityCode0
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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