Video segmentation via boundary-aware flow

We present a new algorithm for unsupervised video segmentation based on boundary-aware optical flow. Existing video segmentation methods usually tweak their segmentation model to tolerate the inaccuracy in the estimation of optical flow around object boundaries. In contrast, we directly manipulate the optical flow for better quality. We smooth the optical flow via transductive inference to make the flow consistent within the object and fit to the object boundaries. We then use the boundary-aware optical flow to estimate the initial foreground object region from each frame for learning the appearance model. The learned appearance model is consequently used to refine the segmentation result. Experiments on the DAVIS dataset show that our method performs favorably against the existing ones.