Video background tracking and foreground extraction via L1-subspace updates

We consider the problem of online foreground extraction from compressed-sensed (CS) surveillance videos. A technically novel approach is suggested and developed by which the background scene is captured by an L1- norm subspace sequence directly in the CS domain. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to outliers, disturbances, and rank selection. Subtraction of the L1-subspace tracked background leads then to effective foreground/moving objects extraction. Experimental studies included in this paper illustrate and support the theoretical developments.