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

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

doi:10.1145/3488933.3488934

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 proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
Publisher	Association for Computing Machinery (ACM)
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781450384087
DOIs	https://doi.org/10.1145/3488933.3488934
Publication status	Published - 24 Sept 2021
Event	4th International Conference on Artificial Intelligence and Pattern Recognition - Virtual, Online, China Duration: 24 Sept 2021 → 26 Sept 2021 Conference number: 4 https://dl.acm.org/doi/proceedings/10.1145/3488933

Conference

Conference	4th International Conference on Artificial Intelligence and Pattern Recognition
Abbreviated title	AIPR 2021
Country/Territory	China
City	Virtual, Online
Period	24/09/21 → 26/09/21
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3488933

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SDG 9 Industry, Innovation, and Infrastructure

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10.1145/3488933.3488934Licence: Unspecified

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@inproceedings{8de2d0cb94c3477399f86bd414bda1e2,

title = "An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network",

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. ",

keywords = "Attention, Object tracking, Regional proposal networks, Siamese network, UAV video",

author = "Yuhuan Zheng and Dianwei Wang and Pengfei Han and Xincheng Ren and Zhijie Xu",

note = "Funding Information: This work was supported in part by the Special Project of Strengthening Police With Science and Technology of Ministry of public security under Grant Nos.2019GABJC42 and the Fund of Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data, Yan{\textquoteright}an University under Grant Nos.IPBED6, and the Graduate Innovation Fund of Xi{\textquoteright}an University of Posts and Telecommunications No.CXJJLY202025. Funding Information: This work was supported in part by the Special Project of Strengthening Police With Science and Technology of Ministry of public security under Grant Nos.2019GABJC42 and the Fund of Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data, Yan an University under Grant Nos.IPBED6, and the Graduate Innovation Fund of Xi an University of Posts and Telecommunications No.CXJJLY202025. Publisher Copyright: {\textcopyright} 2021 ACM.; 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021 ; Conference date: 24-09-2021 Through 26-09-2021",

year = "2021",

month = sep,

day = "24",

doi = "10.1145/3488933.3488934",

language = "English",

pages = "1--8",

booktitle = "AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/3488933",

}

Zheng, Y, Wang, D, Han, P, Ren, X & Xu, Z 2021, An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network. in AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition. Association for Computing Machinery (ACM), pp. 1-8, 4th International Conference on Artificial Intelligence and Pattern Recognition, Virtual, Online, China, 24/09/21. https://doi.org/10.1145/3488933.3488934

An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network. / Zheng, Yuhuan; Wang, Dianwei; Han, Pengfei et al.
AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition. Association for Computing Machinery (ACM), 2021. p. 1-8.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network

AU - Zheng, Yuhuan

AU - Wang, Dianwei

AU - Han, Pengfei

AU - Ren, Xincheng

AU - Xu, Zhijie

N1 - Conference code: 4

PY - 2021/9/24

Y1 - 2021/9/24

N2 - 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.

AB - 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.

KW - Attention

KW - Object tracking

KW - Regional proposal networks

KW - Siamese network

KW - UAV video

UR - https://www.scopus.com/pages/publications/85125869866

U2 - 10.1145/3488933.3488934

DO - 10.1145/3488933.3488934

M3 - Conference contribution

AN - SCOPUS:85125869866

SP - 1

EP - 8

BT - AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

PB - Association for Computing Machinery (ACM)

T2 - 4th International Conference on Artificial Intelligence and Pattern Recognition

Y2 - 24 September 2021 through 26 September 2021

ER -

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

Abstract

Conference

UN SDGs

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