While taking EE364b: Convex Optimization II with Stephen Boyd, Ivan Papusha decided to do the following project:
... explores several large-scale methods for solving the Robust PCA problem via Principal Component Pursuit (PCP), a convex optimization problem, and demonstrates its use in background extraction tasks in video and point-cloud LIDAR data.
The idea is to perform background subtraction on a video stream. The PCP problem admits natural decomposition into Alternating Direction Method of Multipliers (ADMM, the course notes on ADMM are here) form. Here is the result: ‘‘Fast Automatic Background Extraction via Robust PCA’’. The poster is here. The matlab implementation is here.
Thanks Ivan for sharing.
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