HTTrack: Learning to Perceive Targets via Historical Trajectories in Satellite Video Tracking
Jiahao Wang, Fang Liu, Licheng Jiao, Hao Wang, Shuo Li, Xinyi Wang, Lingling Li, Puhua Chen
Abstract
In recent years, the rapid progress of deep learning has driven notable advancements in satellite video tracking, a critical task for applications such as environmental monitoring, disaster management, and defense. Despite these strides, existing approaches remain constrained by their inability to handle dynamic challenges, such as target appearance variations, complex motion patterns, and occlusions. Traditional methods often suffer from static template matching or overly complex update mechanisms, compromising their robustness and practicality in real-world scenarios. To address these limitations, we propose a paradigm shift in satellite video tracking by integrating historical trajectory knowledge with visual features. This fusion enhances the tracker
BibTeX
@inproceedings{aaai2026_httracklearningt,
title = {HTTrack: Learning to Perceive Targets via Historical Trajectories in Satellite Video Tracking},
author = {Jiahao Wang and Fang Liu and Licheng Jiao and Hao Wang and Shuo Li and Xinyi Wang and Lingling Li and Puhua Chen and Xu Liu},
booktitle = {AAAI 2026},
year = {2026}
}