  Siamese-based trackers have achived promising performance on visual object
tracking tasks. Most existing Siamese-based trackers contain two separate
branches for tracking, including classification branch and bounding box
regression branch. In addition, image segmentation provides an alternative way
to obetain the more accurate target region. In this paper, we propose a novel
tracker with two-stages: detection and segmentation. The detection stage is
capable of locating the target by Siamese networks. Then more accurate tracking
results are obtained by segmentation module given the coarse state estimation
in the first stage. We conduct experiments on four benchmarks. Our approach
achieves state-of-the-art results, with the EAO of 52.6$\%$ on VOT2016,
51.3$\%$ on VOT2018, and 39.0$\%$ on VOT2019 datasets, respectively.
