Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 5
1587-1591,1596

Person re-identification by decorrelated high-precision classification network and re-ranking

Han Guang
Ge Yaming
Zhang Chengwei
Engineering Research Center of Wideband Wireless Communication Technique of Ministry of Education, Nanjing University of Posts & Telecommunications, Nanjing 210003, China
DOI: 10.19734/j.issn.1001-3695.2018.12.0918

Abstract

At present, the pedestrian re-identification method based on deep learning usually trained the classification network as the basic network, then used it to extract the deep features of the pedestrian images. Finally, calculated the similarities between features under the Euclidean distance metric and established a ranking table. Therefore, the feature representation ability of the classification network would affect the accuracy of re-identification, and the correlation between the feature representations would also cause errors in the similarity calculation. Aiming at these problems, this paper adopted a classification network with higher precision as the feature extraction network for pedestrian re-identification, and used singular value decomposition to reduce the correlation between weight vectors and optimized the deep learning process. In addition, it used the K-reciprocal encoding method to re-rank the pictures that need to be retrieved in the gallery that will further improve the accuracy of reidentification. Experimental results on the Market-1501 dataset show that this method can significantly improve the accuracy of pedestrian re-identification.

Foundation Support

国家自然科学基金资助项目(61871445,61302156)
江苏省重点研发基金资助项目(BE2016001-4)
教育部—中国移动科研基金资助项目(MCM20150504)
江苏省高校自然科学研究资助项目(13KJB510021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.12.0918
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 5
Section: Technology of Graphic & Image
Pages: 1587-1591,1596
Serial Number: 1001-3695(2020)05-064-1587-05

Publish History

[2020-05-05] Printed Article

Cite This Article

韩光, 葛亚鸣, 张城玮. 基于去相关高精度分类网络与重排序的行人再识别 [J]. 计算机应用研究, 2020, 37 (5): 1587-1591,1596. (Han Guang, Ge Yaming, Zhang Chengwei. Person re-identification by decorrelated high-precision classification network and re-ranking [J]. Application Research of Computers, 2020, 37 (5): 1587-1591,1596. )

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  • 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.

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