  The current Siamese network based on region proposal network (RPN) has
attracted great attention in visual tracking due to its excellent accuracy and
high efficiency. However, the design of the RPN involves the selection of the
number, scale, and aspect ratios of anchor boxes, which will affect the
applicability and convenience of the model. Furthermore, these anchor boxes
require complicated calculations, such as calculating their
intersection-over-union (IoU) with ground truth bounding boxes.Due to the
problems related to anchor boxes, we propose a simple yet effective anchor-free
tracker (named Siamese corner networks, SiamCorners), which is end-to-end
trained offline on large-scale image pairs. Specifically, we introduce a
modified corner pooling layer to convert the bounding box estimate of the
target into a pair of corner predictions (the bottom-right and the top-left
corners). By tracking a target as a pair of corners, we avoid the need to
design the anchor boxes. This will make the entire tracking algorithm more
flexible and simple than anchorbased trackers. In our network design, we
further introduce a layer-wise feature aggregation strategy that enables the
corner pooling module to predict multiple corners for a tracking target in deep
networks. We then introduce a new penalty term that is used to select an
optimal tracking box in these candidate corners. Finally, SiamCorners achieves
experimental results that are comparable to the state-of-art tracker while
maintaining a high running speed. In particular, SiamCorners achieves a 53.7%
AUC on NFS30 and a 61.4% AUC on UAV123, while still running at 42 frames per
second (FPS).
