A Bayesian Computer Vision System for Modeling Human Interaction
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computing and Applications - Special Issue - KES2008
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Robust principal component analysis?
Journal of the ACM (JACM)
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Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. RPCA problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l1-norm. To solve this convex program, an Alternating Direction Method (ADM) is commonly used. However, the subproblems in ADM are easily solvable only when the linear mappings in the constraints are identities. This assumption is rarely verified in real application such as foreground detection. In this paper, we propose to use a Linearized Alternating Direction Method (LADM) with adaptive penalty to achieve RPCA for foreground detection. LADM alleviates the constraints of the original ADM with a faster convergence speed. Experimental results on different datasets show the pertinence of the proposed approach.