Object Tracking in Satellite Videos Based on a Lightweight Network

Object tracking is a hot topic in computer vision. The significantly developed satellite video technology makes object tracking in satellite videos possible. In recent years, Convolutional Neural Network (CNN)-based trackers have achieved satisfying performance in the visual object tracking field. However, CNN cannot be directly applied to object tracking in satellite videos due to the following two reasons. First, the feature map size generally decreases as the network layer deepens, which is unsuitable for the small targets in satellite videos. Second, CNN-based trackers commonly need extensive data to train the network parameters, while few labeled satellite videos are available. Therefore, in this paper, we design a lightweight network for the satellite video tracking task. On one hand, the network generates a response map with the same size as the input image and reserves the spatial resolution of targets. On the other hand, the network parameters are transferred from an existing network and trained with the initial annotated frame, thus no extra data are needed. To make a fair comparison between existing trackers, we further propose a simulated benchmark based on the UAV123 dataset according to the imaging characteristics of satellite videos. Experiments are conducted to compare our method with other state-of-the-art trackers on both the simulated benchmark and real satellite videos and experimental results demonstrate the superiority of our proposed algorithm.