  Recently, we have seen a rapid development of Deep Neural Network (DNN) based
visual tracking solutions. Some trackers combine the DNN-based solutions with
Discriminative Correlation Filters (DCF) to extract semantic features and
successfully deliver the state-of-the-art tracking accuracy. However, these
solutions are highly compute-intensive, which require long processing time,
resulting unsecured real-time performance. To deliver both high accuracy and
reliable real-time performance, we propose a novel tracker called
SiamVGG\footnote{https://github.com/leeyeehoo/SiamVGG}. It combines a
Convolutional Neural Network (CNN) backbone and a cross-correlation operator,
and takes advantage of the features from exemplary images for more accurate
object tracking. The architecture of SiamVGG is customized from VGG-16 with the
parameters shared by both exemplary images and desired input video frames. We
demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017
datasets with the state-of-the-art accuracy while maintaining a decent
real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve
2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in
VOT2017 Challenge.
