Study of UAV tracking based on CNN in noisy environment
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Study of UAV tracking based on CNN in noisy environment Zhuojin Sun1 · Yong Wang2
´ 4 · Chen Gong3 · Robert Laganiere
Received: 8 October 2019 / Revised: 17 August 2020 / Accepted: 25 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Recently, there are lots of tracking methods proposed to improve the performance of visual tracking in videos with challenging situations, such as background clutter, severe occlusion, rotation, and so on. In real unmanned aerial vehicle (UAV) based tracking systems, there are various noises occurring during video capturing, transmission, and processing. However, most existing studies pay attention to improve the robustness and accuracy of visual tracking while ignoring the performance of tracking methods on videos with noise. In this paper, we investigate the performance evaluation of existing tracking methods on videos with noise. A group of noisy UAV based tracking video datasets are constructed and used to the benchmark datasets for analysis of tracking methods. Furthermore, we propose an algorithm for robustness tracking in noisy videos. The performance of 9 tracking methods is evaluated on the proposed dataset. We provide the detailed analysis and discussion on the robustness analysis of different tracking methods on videos with different variance of noises. Our investigation shows that it is still challenging for effective tracking for existing methods on videos with noise. And our proposed method shows promising results in noisy videos. Keywords Noisy videos · UAV tracking · CNN feature based tracker
This research is partially supported by NSF of China (No: 61602246, 61973162), NSF of Jiangsu Province (No: BK20171430), the Fundamental Research Funds for the Central Universities (No: 30918011319), and the “Summit of the Six Top Talents” Program (No: DZXX-027), the Young Elite Scientists Sponsorship Program by Jiangsu Province, and the Young Elite Scientists Sponsorship Program by CAST (No: 2018QNRC001) Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11042-020-09713-9) contains supplementary material, which is available to authorized users. Yong Wang
[email protected] 1
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
2
School of Aeronautics and Astronautics, Sun Yat-Sen University, Guangzhou, China
3
The PCA Lab, the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, P.R. China
4
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Multimedia Tools and Applications
1 Introduction Unmanned aerial vehicles (UAVs) with amounted camera is becoming a very important research direction as the increasing commercially UAVs. In fact, UAV based tracking is a very promising application as the UAV can follow the object based on visual fee
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