Siamese Network for Object Tracking in Aerial Video

In Unmanned Aerial Vehicle (UAV) videos, object tracking remains a challenge, due to its low spatial resolution and poor real-time performance. Recently, methods of deep learning have made great progress in object tracking in computer vision, especially fully-convolutional siamese neural networks (SiamFC). Inspired by it, this paper aims to investigate the use of SiamFC for object tracking in UAV videos. The network is trained on part of a UAV123 dataset and Stanford Drone dataset. First, exemplar image is extracted from the first frame and search regions are extracted in the following frames. Then, a Siamese network is used for tracking objects by calculating the similarity between exemplar image and search region. To evaluate our method, we test on a challenge VIVID dataset. The experiment shows that the proposed method has improvements in accuracy and speed in low spatial resolution UAV videos compared to existing methods.