1. A method for segmenting and tracking content in a video stream, wherein the video includes a sequence of images, comprising, for each set of one or more images in the video stream, at each time step: determining measured data from the set; initializing a parameter and a low-dimensional subspace characterizing motion of the content in the measured data, wherein the parameter is an adaptive step-size parameter; determining a sparse vector representing a sparse component that characterizes the motion of the content different from the motion of the content characterized by the low-dimensional subspace; determining a change in the low-dimensional subspace for the measured data using a proximal point iteration and the parameter; updating the parameter according to the change; updating a low-rank subspace matrix representing the low-dimensional subspace according to the change and the parameter; and outputting the low-rank matrix representing the low-dimensional subspace, and the sparse vector, wherein the steps are performed in a processor connected to a memory storing the measured data, and the video stream is acquired of a scene by a camera.