The Department of Computer Science at the University of Cyprus cordially invites you to the Colloquium entitled:
Incremental Robust Principal Component Analysis for Video Background Modeling: theory, applications and jitter invariant extension.
Speaker: Dr. Paul Rodriguez
While Robust Principal Component Analysis (RPCA), a.k.a. Principal Component Pursuit (PCP), is currently considered to be the state of the art method for video background modeling, it suffers from a number of limitations, including a high computational cost, a batch operating mode, and sensitivity to camera jitter. The original PCP problem considers the nuclear and l1 norms as penalties for the background (low-rank) and foreground or moving objects (sparse) with an equality constraint for the observed videos and low-rank and sparse components. In this talk we propose to change constraints to penalties, obtaining a variant where the restoration error (observed video minus low-rank and sparse component) and l1 norm are penalties while imposing the rank of the low-rank component as a constraint. Interestingly, this particular variant can be effectively solved in an incremental fashion, allowing real-time implementation for live-feed HD videos; moreover, considering T(.), an unknown rigid transformation, applied to the low-rank component, we can also cope with translational and rotational jitter, allowing almost real-time processing. Furthermore, in this talk we will also include a detailed analysis of the proposed RPCA variant as well as incremental SVD, which is the key to solve the equivalent problem incrementally.
Paul Rodriguez received the BSc degree in electrical engineering from the "Pontificia Universidad Católica del Perú" (PUCP), Lima, Peru, in 1997, and the MSc and PhD degrees in electrical engineering from the University of New Mexico, USA, in 2003 and 2005 respectively. He spent two years (2005-2007) as a postdoctoral researcher at Los Alamos National Laboratory, and is currently a Full Professor with the Department of Electrical Engineering at PUCP. His research interests include AM-FM models, parallel algorithms, adaptive signal decompositions, and inverse problems in signal and image processing.
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