A Comparison between Background Subtraction Algorithms using a Consumer Depth Camera

Background subtraction is an important preprocessing step in many modern Computer Vision systems. Much work has been done especially in the field of color image based foreground segmentation. But the task is not an easy one so, state of the art background subtraction algorithms are complex both in programming logic and in run time. Depth cameras might offer a compelling alternative to those approaches, because depth information seems to be better suited for the task. But this topic has not been studied much yet, even though the release of Microsoft’s Kinect has brought depth cameras to the public attention. In this paper we strive to fill this gap, by examining some well known background subtraction algorithms for the use with depth images. We propose some necessary adaptions and evaluate them on three different video sequences using ground truth data. The best choice turns out to be a very simple and fast method that we call minimum background.