Fully Conventional Anchor-Free Siamese Networks for Object Tracking

Abstract

Siamese network have been extensively applied in the tracking field because of its huge speed advantage and great precision performance in solving the tracking problems. In this paper, we propose an efficient framework for real-time object tracking which is end-to-end trained offline-Fully Conventional Anchor-Free Siamese network (FCAF). Specifically, as the backbone network in Siamese trackers is relatively shallow, resulting in insufficient feature information acquired by the trackers and lower accuracy, the deep network ResNet-50 is adopted to provide richer feature representation. Meanwhile, the introduction of multi-layer feature fusion module effectively combines low-level detail information with high-level semantic features, improving the localization performance. In addition, we propose the anchor-free proposal network (AFPN) to replace the region proposal network (RPN). AFPN network consists of correlation section, implemented by depth-wise cross correlation, and supervised section which has two branches, one for classification and the other for regression. In order to suppress the prediction of low quality bounding boxes, center-ness branch is added. We conduct extensive experiments on the OTB2015 and VOT2016 public datasets, demonstrating that our proposed tracker achieves state-of-the-art performance.