The technique has been used in many vision systems as a preprocessing step for object detection and tracking. However, many of these algorithms are susceptible to both global and local illumination changes such as shadows and highlights. These cause the consequent processes, e.g. tracking, recognition, etc., to fail. The accuracy and efficiency of the detection are clearly very crucial to those tasks. The basic scheme of background subtraction is to subtract the image from a reference image that models the background scene.
Background modeling constructs a reference image representing the background.
Threshold selection determines appropriate threshold values used in the subtraction operation to obtain a desired detection rate. In this project, the student will first study different methods of classical background subtraction on grayscale images. After discussion, one of those algorithms will be implemented and tested. Depending on the test results a more advanced method, maybe using color information, will be chosen and implemented.