  Single object tracking (SOT) is currently one of the most important tasks in
computer vision. With the development of the deep network and the release for a
series of large scale datasets for single object tracking, siamese networks
have been proposed and perform better than most of the traditional methods.
However, recent siamese networks get deeper and slower to obtain better
performance. Most of these methods could only meet the needs of real-time
object tracking in ideal environments. In order to achieve a better balance
between efficiency and accuracy, we propose a simpler siamese network for
single object tracking, which runs fast in poor hardware configurations while
remaining an excellent accuracy. We use a more efficient regression method to
compute the location of the tracked object in a shorter time without losing
much precision. For improving the accuracy and speeding up the training
progress, we introduce the Squeeze-and-excitation (SE) network into the feature
extractor. In this paper, we compare the proposed method with some
state-of-the-art trackers and analysis their performances. Using our method, a
siamese network could be trained with shorter time and less data. The fast
processing speed enables combining object tracking with object detection or
other tasks in real time.
