  An increasing number of methods for background subtraction use Robust PCA to
identify sparse foreground objects. While many algorithms use the L1-norm as a
convex relaxation of the ideal sparsifying function, we approach the problem
with a smoothed Lp-norm and present pROST, a method for robust online subspace
tracking. 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 subtraction
on real-world video data indicate that the method succeeds at a broad variety
of background subtraction scenarios, and it outperforms competing approaches
when video quality is deteriorated by camera jitter.
