SiamTITP: Incorporating Temporal Information and Trajectory Prediction Siamese Network for Satellite Video Object Tracking.

Abstract

Object tracking is considered as a template matching task. Traditional and deep learning-based methods have achieved high performance in satellite video object tracking (SVOT). However, existing methods still suffer from insufficiently discriminative features, complex approaches to handling occlusion, and excessive hyperparameters. In response to these issues, we propose a simple, yet effective Siamese network, termed SiamTITP. A temporal information (TI) submodule is developed, which integrates temporal cues by dynamically updating the template to enhance discriminative features. Furthermore, we propose a structurally simple trajectory prediction (TP) submodule, which solely utilizes polynomial function for fitting historical results to assist the network in addressing occlusion. In an effort to reduce hyperparameters, we forgo feature fusion steps and weighted results, while we propose an adaptive occlusion judgment metrics based on the target size. To validate the efficacy of our approach, we conducted extensive experiments on three large satellite video datasets, namely the SatSOT, SV248S and OOTB datasets. Code and train models are publicly available at https://github.com/jiawei-zhou/SiamTITP.