Offline generation of high quality background subtraction data

Ground truth is important not only for performance evaluation but also for a principled development of computer vision algorithms. Unfortunately obtaining ground truth data is difficult and often very labor intensive. This is particularly true of video analysis due to the immense cost of producing pixel-wise ground truth in potentially thousands of frames. In this paper, we propose a method to produce foreground/background segmentation for video sequences captured by a stationary camera, that requires very little human labor as compared to complete manual segmentation, while still producing high quality results. Given a sequence, we use a few hand labeled images and Adaboost to train a classifier that segments the rest of the sequence. We demonstrate the effectiveness of our approach on two sequences and discuss the new horizons opened by these encouraging results.