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 <i>L</i><sub>1</sub>- norm subspace sequence directly in the CS domain. In contrast to conventional <i>L</i><sub>2</sub>-norm subspaces, <i>L</i><sub>1</sub>-norm subspaces are seen to offer significant robustness to outliers, disturbances, and rank selection. Subtraction of the <i>L</i><sub>1</sub>-subspace tracked background leads then to effective foreground/moving objects extraction. Experimental studies included in this paper illustrate and support the theoretical developments.