Leveraging cross-view geo-localization with ensemble learning and temporal awareness

被引:0
|
作者
Ghanem, Abdulrahman [1 ]
Abdelhay, Ahmed [1 ]
Salah, Noor Eldeen [1 ]
Nour Eldeen, Ahmed [1 ]
Elhenawy, Mohammed [2 ]
Masoud, Mahmoud [3 ]
Hassan, Ammar M. M. [4 ]
Hassan, Abdallah A. A. [1 ]
机构
[1] Minia Univ, Fac Engn, Comp & Syst Engn Dept, Al Minya, Egypt
[2] Queensland Univ Technol, Ctr Accid Res & Rd Safety Queensland CARRS Q, Brisbane, Australia
[3] King Fahd Univ Petr & Minerals, Interdisciplinary Res Ctr Smart Mobil & Logist, Dept Informat Syst & Operat Management, Dhahran, Saudi Arabia
[4] Arab Acad Sci Technol & Maritime Transport, South Valley Branch, Aswan, Egypt
来源
PLOS ONE | 2023年 / 18卷 / 03期
关键词
D O I
10.1371/journal.pone.0283672
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The Global Navigation Satellite System (GNSS) is unreliable in some situations. To mend the poor GNSS signal, an autonomous vehicle can self-localize by matching a ground image against a database of geotagged aerial images. However, this approach has challenges because of the dramatic differences in the viewpoint between aerial and ground views, harsh weather and lighting conditions, and the lack of orientation information in training and deployment environments. In this paper, it is shown that previous models in this area are complementary, not competitive, and that each model solves a different aspect of the problem. There was a need for a holistic approach. An ensemble model is proposed to aggregate the predictions of multiple independently trained state-of-the-art models. Previous state-of-the-art (SOTA) temporal-aware models used heavy-weight network to fuse the temporal information into the query process. The effect of making the query process temporal-aware is explored and exploited by an efficient meta block: naive history. But none of the existing benchmark datasets was suitable for extensive temporal awareness experiments, a new derivative dataset based on the BDD100K dataset is generated. The proposed ensemble model achieves a recall accuracy R@1 (Recall@1: the top most prediction) of 97.74% on the CVUSA dataset and 91.43% on the CVACT dataset (surpassing the current SOTA). The temporal awareness algorithm converges to R@1 of 100% by looking at a few steps back in the trip history.
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页数:23
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