当前位置: X-MOL 学术Int. J. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Online collaboration-based visual tracking for unmanned aerial vehicle with spatial-to-semantic information and multi-recommender voting.
International Journal of Remote Sensing ( IF 3.4 ) Pub Date : 2020-12-20 , DOI: 10.1080/01431161.2020.1847346
Changhong Fu 1 , Yiming Li 1 , Ziyuan Huang 2 , Yiyong Sun 2 , Jianyu Yang 3
Affiliation  

ABSTRACT Object tracking plays a crucial role in remote sensing for the unmanned aerial vehicle (UAV). In recent years, deep learning contributes hugely to the visual object tracking, and one typical application is that deep features extracted from convolutional neural networks are widely employed for robust representations of the tracked object, as early layers retain higher spatial accuracy and the latter ones contain more semantic information. However, the potential of deep features as well as their fusion has not been thoroughly achieved. In order to fully utilize multi-level deep features, multiple recommenders based on discriminative correlation filters are constructed in this work and provided with a combination of deep features from different layers. Each recommender tracks the object independently and its reliability is evaluated based on the voting from other recommenders as well as from itself. The result of the recommender evaluated as the best will be learned by others adaptively. Extensive experiments on 100 challenging UAV image sequences have demonstrated that the proposed method outperforms recently developed 25 state-of-the-art trackers in terms of robustness and accuracy.

中文翻译:

具有空间语义信息和多推荐人投票的无人机在线协作视觉跟踪。

摘要 目标跟踪在无人机 (UAV) 的遥感中起着至关重要的作用。近年来,深度学习对视觉对象跟踪做出了巨大贡献,其中一个典型应用是从卷积神经网络中提取的深度特征被广泛用于对被跟踪对象的鲁棒表示,因为早期层保留了更高的空间精度,而后者包含更多的语义信息。然而,深度特征的潜力以及它们的融合尚未完全实现。为了充分利用多层次的深度特征,本文构建了多个基于判别相关滤波器的推荐器,并提供了来自不同层的深度特征的组合。每个推荐者独立跟踪对象,并根据其他推荐者和自身的投票评估其可靠性。推荐人评估为最好的结果将被其他人自适应地学习。对 100 个具有挑战性的无人机图像序列的大量实验表明,所提出的方法在鲁棒性和准确性方面优于最近开发的 25 个最先进的跟踪器。
更新日期:2020-12-20
down
wechat
bug