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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1237-1241,1246

Multi-layer feature fusion and parallel self-attention Siamese networks for visual tracking

Shu Ping
Xu Keying
Bao Hua
School of Electrical Engineering & Automation, Anhui University, Hefei 230601, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0330

Abstract

Object tracking is an important topic in computer visual directions, where scale changes, deformation and rotation are difficult to resolve in the field. For the challenging problems faced in the above track, based on existing Siamese network algorithms, this paper proposed multi-layer feature fusion and parallel self-attention Siamese networks(MPSiamRPN) for visual tracking. Firstly, MPSiamRPN used the modified ResNet50 to extract features from the template image and the search image. In order to deal with the loss of some features caused by the deep network, it proposed a multi-layer feature fusion module to fuse the features of the last three layers of ResNet. Secondly, it introduced the parallel self-attention module, which was composed of channel self-attention and spatial self-attention. The channel self-attention could selectively emphasize the beneficial channel features for tracking, and the spatial self-attention could learn the rich spatial information of the target. Finally, it proposed the region proposal network(RPN) to perform classification and regression operations to determine the location and shape of the target. Experiments show that the MPSiamRPN can achieve competitive results on OTB100 and VOT2018 test datasets.

Foundation Support

安徽省自然科学基金资助项目(1908085MF217)
安徽省教育厅自然科学重点资助项目(KJ2019A0022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0330
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Technology of Graphic & Image
Pages: 1237-1241,1246
Serial Number: 1001-3695(2022)04-047-1237-05

Publish History

[2021-11-22] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

束平, 许克应, 鲍华. 多层特征融合和并行自注意力的孪生网络目标跟踪算法 [J]. 计算机应用研究, 2022, 39 (4): 1237-1241,1246. (Shu Ping, Xu Keying, Bao Hua. Multi-layer feature fusion and parallel self-attention Siamese networks for visual tracking [J]. Application Research of Computers, 2022, 39 (4): 1237-1241,1246. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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