# 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.

# FlowNet3D: Learning Scene Flow in 3D Point Clouds

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.

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# Sparse Subspace Clustering: Algorithm, Theory, and Applications

5 Mar 2012

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.

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# EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.

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# Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm

The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting.

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# Moving Objects Detection with a Moving Camera: A Comprehensive Review

15 Jan 2020

During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments.

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# Learning Articulated Motions From Visual Demonstration

5 Feb 2015

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.

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# Robust Subspace Clustering via Smoothed Rank Approximation

18 Aug 2015

However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.

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# 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).

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# Robust Subspace Clustering via Tighter Rank Approximation

30 Oct 2015

For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.

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# 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.

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