Paper Info
Title
Low-Rank Spatio-Temporal Video Segmentation.
Abstract
Recently, a great deal of interest has been generated by the technique known as Robust Principle Component Analysis (RPCA) of Candes et al. [1], which addresses the problem of separating a matrix into a low-rank and a sparse component. This very general formulation can be used for tasks such as background estimation in videos and face recognition. In the case of background estimation, the low-rank matrix models the background, and the sparse matrix corresponds to the foreground. A considerable drawback of this approach is its poor robustness to local lighting conditions. If lighting conditions vary locally, one of two things may happen. Either the method incorporates the lighting variation into the foreground, which is clearly undesirable, or the rank of the background model is allowed to increase. Unfortunately, this second option means that the true foreground is likely to become included in the background, especially for objects which are static for a short while. Here, we propose to model the background as a piece-wise low-rank matrix. In this manner, it will be possible to extract several localised models which correspond to coherent lighting conditions. However, for this we need to segment the input video into such coherent regions. We refer to this problem as a low-rank spatio-temporal video segmentation. We present an algorithm to address this segmentation problem, based on region merging and spectral clustering techniques. We show that by carrying out a local RPCA in each region, the results of foreground/background separation are greatly improved, in comparison with both the standard RPCA and several other well-known background estimation techniques. Let X ∈ Rm×n represent an input video, in matrix form. Each frame contains m pixels, and there are a total of n frames in our video. The goal of RPCA is to decompose X as X≈ L+S, where L is the low-rank matrix and S is the sparse matrix. Unfortunately, the rank of a matrix is a non-convex function, so a surrogate function, the nuclear norm is used. Thus, the background/foreground separation problem may be formulated as follows:
Year
Venue
Field
2015
BMVC
Rank (linear algebra),Computer vision,Spectral clustering,Scale-space segmentation,Pattern recognition,Computer science,Matrix (mathematics),Segmentation-based object categorization,Matrix norm,Artificial intelligence,Video denoising,Sparse matrix
DocType
Citations 
PageRank 
Conference
2
0.35
References 
Authors
7
3
Name
Order
Citations
PageRank
Alasdair Newson1677.00
Mariano Tepper26812.80
Guillermo Sapiro3148131051.92