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 141–150 of 212 papers

TitleStatusHype
Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information—0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes—0
Segmenting the motion components of a video: A long-term unsupervised model—0
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure—0
Using Motion and Internal Supervision in Object Recognition—0
Video Motion Segmentation Using New Adaptive Manifold Denoising Model—0
Video Segmentation With Just a Few Strokes—0
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process—0
WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge—0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
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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