Silhouette extraction based on iterative spatio-temporal local color transformation and graph-cut segmentation

We propose an iterative scheme of spatio-temporal local color transformation of background and graph-cut segmentation for silhouette extraction. Given an initial background subtraction, spatio-temporal background color transformation is processed for fitting modeled background colors to input background ones under a different illumination condition. After foreground colors are modeled based on the fit background, spatio-temporal graph-cut algorithm is applied to acquire a foreground/background segmentation result. Because these two processes need well-segmented background and well-fit background each other, they are iterated in turn to obtain better silhouette extraction results. Silhouette extraction experiments for a walking human on a treadmill show the effectiveness of the proposed method.