An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network

Yuhuan Zheng, Dianwei Wang, Pengfei Han, Xincheng Ren, Zhijie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Unmanned Aerial Vehicle (UAV) has been widely used in military and civilian fields, and object tracking is one of the critical technologies in UAV application. For addressing deformation, occlusion, small object, and other UAV object tracking problems, an UAV video object tracking algorithm based on Siamese Attention Network (SANet) is proposed in this paper. Initially, we designed a lightweight network as an extractor to extract features. After that, the attention mechanism module is constructed to screen out the feature map's semantic attributes, and the corresponding weights are re-assigned to different channels and spatial features. Finally, three Regional Proposal Networks (RPNs) are introduced to hierarchical fusion to obtain the tracking results. Our proposed algorithm in this paper has experimented on the UAV123 dataset and self-built dataset. The results show that the algorithm has a good tracking effect, the average accuracy is improved to 0.815, and the success rate is 0.619.

Original languageEnglish
Title of host publicationAIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
PublisherAssociation for Computing Machinery (ACM)
Pages1-8
Number of pages8
ISBN (Electronic)9781450384087
DOIs
Publication statusPublished - 24 Sep 2021
Event4th International Conference on Artificial Intelligence and Pattern Recognition - Virtual, Online, China
Duration: 24 Sep 202126 Sep 2021
Conference number: 4
https://dl.acm.org/doi/proceedings/10.1145/3488933

Conference

Conference4th International Conference on Artificial Intelligence and Pattern Recognition
Abbreviated titleAIPR 2021
Country/TerritoryChina
CityVirtual, Online
Period24/09/2126/09/21
Internet address

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