With the development of applications associated to ego-vision systems, smart-phones, and autonomous cars, automated analysis of videos generated by freely moving cameras has become a major challenge for the computer vision community. Current techniques are still not suitable to deal with real-life situations due to, in particular, wide scene variability and the large range of camera motions. Whereas most approaches attempt to control those parameters, this paper introduces a novel video analysis paradigm, 'vide-omics', inspired by the principles of genomics where variability is the expected norm. Validation of this new concept is performed by designing an implementation addressing foreground extraction from videos captured by freely moving cameras. Evaluation on a set of standard videos demonstrates both robust performance that is largely independent from camera motion and scene, and state-of-the-art results in the most challenging video. Those experiments underline not only the validity of the 'vide-omits' paradigm, but also its potential.