50 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.
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.
To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments.
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy.
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.
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).
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.