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Friday, March 20, 2015
10:00 AM - 11:00 AM
CNLS Conference Room (TA-3, Bldg 1690)


Incremental Principal Principal Component Pursuit for Video Background Modeling: theory, applications and jitter invariant extension

Paul Rodriguez-Valderrama
Pontificia Universidad Catolica Del Peru

While Principal Component Pursuit (PCP), a.k.a. Robust Principal Component Analysis (RPCA), 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 constrain 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 PCP variant as well as incremental SVD, which is the key to solve the equivalent problem incrementally.

Host: Brendt Wholberg