Coarse-to-Fine Siamese Network for Tooth Object Tracking

Abstract

At present, object tracking algorithms based on deep learning have been widely used in the computer vision industry and has great application value. In this paper, we track the tooth, which has the problem of poor texture and high similarity, with the object tracking algorithm. In general, previously existing single object tracking algorithms are derived from the siamese network architecture. These algorithms have the problem of losing a tracked object with poor textures and high similarities. In order to address the issue of object loss, we put forward a novel coarse-to-fine siamese network (CFSN) for tooth object tracking. The coarse detection network is based on the features extracted by the Feature Pyramid Networks (FPN) and the Region Proposal Network (RPN). The fine detection network has a similar structure to the coarse detection network, and the main purpose is to fine-tune the results of the previous stage. Based on the coarse-to-fine network, a framework of multi-task is proposed, that is, the mask prediction branch is added to the fine detection network, which lead to significant improvements in tracking efficiency. Through the comprehensive experimental evaluation of the tooth dataset, we find that the CFSN model is more effective in tracking tooth objects than the existing methods.