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An increasing number of methods for background subtraction use Robust PCA toidentify sparse foreground objects. While many algorithms use the L1-norm as aconvex relaxation of the ideal sparsifying function, we approach the problemwith a smoothed Lp-norm and present pROST, a method for robust online subspacetracking. The algorithm is based on alternating minimization on manifolds.Implemented on a graphics processing unit it achieves realtime performance.Experimental results on a state-of-the-art benchmark for background subtractionon real-world video data indicate that the method succeeds at a broad varietyof background subtraction scenarios, and it outperforms competing approacheswhen video quality is deteriorated by camera jitter.

pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video