Robots operate in a dynamic world in which objects are often moving. The movement of objects may help the robot to segment the objects from the background. The result of the segmentation can subsequently be used to identify the objects. This paper investigates the possibility of segmenting objects of interest from the background for the purpose of identification based on motion. It focusses on two approaches to represent the movements: one based on optical flow estimation and the other based on the SIFT-features. The segmentation is based on the expectation-maximization algorithm. A support vector machine, which classifies the segmented objects, is used to evaluate the result of the segmentation.