pROST: a smoothed $$\ell _p$$ℓp-norm robust online subspace tracking method for background subtraction in video

An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects. While many algorithms use the $$\ell _1$$ℓ1-norm as a convex relaxation of the ideal sparsifying function, we approach the problem with a smoothed $$\ell _p$$ℓp-quasi-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 at a resolution of $$160 \times 120$$160×120. 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.

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