Fast label propagation for real-time superpixels for video content

Many recent superpixel algorithms for video content rely on dense optical flow vectors to propagate segmentation results from one frame to the next. In this paper, we assess the impact of the optical flow quality on the over-segmentation quality. Our evaluation shows that it is indispensable for videos with large object displacement and camera motion. But due to the high computational costs high-quality, dense optical flow is not suitable for real-time applications. Therefore, we propose a fast propagation scheme that is based on sparse feature tracking and mesh-based image warping. In a thorough evaluation, we compare our proposed scheme to the results of other state-of-the-art propagation methods using established benchmarks. The results show that our method speeds up the propagation process by a factor of 100 while producing a comparable segmentation quality.