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 141150 of 212 papers

TitleStatusHype
Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes0
Segmenting the motion components of a video: A long-term unsupervised model0
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure0
Using Motion and Internal Supervision in Object Recognition0
Video Motion Segmentation Using New Adaptive Manifold Denoising Model0
Video Segmentation With Just a Few Strokes0
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process0
WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models0
Show:102550
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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