Motion Segmentation
39 papers with code • 4 benchmarks • 7 datasets
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.
Benchmarks
These leaderboards are used to track progress in Motion Segmentation
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Hopkins155
ApolloScape
EV-IMO
KT3DMoSeg
KITTI'15 MSplus
Most implemented papers
FlowNet3D: Learning Scene Flow in 3D Point Clouds
xingyul/flownet3d
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CVPR 2019
In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.
Sparse Subspace Clustering: Algorithm, Theory, and Applications
panji1990/Deep-subspace-clustering-networks
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In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
mfaisal59/EpONet
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WACV 2020
To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting.
Moving Objects Detection with a Moving Camera: A Comprehensive Review
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments.
Learning Articulated Motions From Visual Demonstration
This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion.
Robust Subspace Clustering via Smoothed Rank Approximation
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i. e., separating points drawn from a union of subspaces).
Robust Subspace Clustering via Tighter Rank Approximation
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking
The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization.
