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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 7
2206-2209

Improved convolutional network target tracking algorithm

Li Gang1
Zhang Yubo1
Sun Jiangyan2
Shen Dan3
1. School of Electronic & Control Engineering, Chang'an University, Xi'an 710064, China
2. School of Engineering, Xi'an International University, Xi'an 710077, China
3. School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China
DOI: 10.19734/j.issn.1001-3695.2019.01.0028

Abstract

Deep networks have been successfully applied to visual tracking by training large numbers of images offline. However, the offline training is time-consuming and the learned vast representation may be less discriminative for tracking specific objects. In this article, although without training deal auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representation for visual tracking. In the first frame, this algorithm extracted a set of normalized patches from the target region as fixed filters, which integrated a series of adaptive filters surrounding the target to define a set of feature maps by extracting the normalized samples of the subsequent frames of convolution operation. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target was also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. The convolution networks have a lightweight structure that is good for methods on the recent tracking benchmark data set with multiple videos.

Foundation Support

西安市科学技术局科技创新引导项目(201805045YD23CG29(5))
中央高校基本科研业务费专项资金项目(300102329203)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.01.0028
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Technology of Graphic & Image
Pages: 2206-2209
Serial Number: 1001-3695(2020)07-060-2206-04

Publish History

[2020-07-05] Printed Article

Cite This Article

李刚, 张宇博, 孙姜燕, 等. 改进的卷积网络目标跟踪算法 [J]. 计算机应用研究, 2020, 37 (7): 2206-2209. (Li Gang, Zhang Yubo, Sun Jiangyan, et al. Improved convolutional network target tracking algorithm [J]. Application Research of Computers, 2020, 37 (7): 2206-2209. )

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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