  Video object segmentation (VOS) is an essential part of autonomous vehicle
navigation. The real-time speed is very important for the autonomous vehicle
algorithms along with the accuracy metric. In this paper, we propose a
semi-supervised real-time method based on the Siamese network using a new polar
representation. The input of bounding boxes is initialized rather than the
object masks, which are applied to the video object detection tasks. The polar
representation could reduce the parameters for encoding masks with subtle
accuracy loss so that the algorithm speed can be improved significantly. An
asymmetric siamese network is also developed to extract the features from
different spatial scales. Moreover, the peeling convolution is proposed to
reduce the antagonism among the branches of the polar head. The repeated
cross-correlation and semi-FPN are designed based on this idea. The
experimental results on the DAVIS-2016 dataset and other public datasets
demonstrate the effectiveness of the proposed method.
