A cross-domain weakly supervised method based on imprecise labels for person RE-ID

Jingmiao Zhu; Fengpeng Guo; Dongyue Chen; Shizhuo Deng

doi:10.1117/12.2668498

20 April 2023 A cross-domain weakly supervised method based on imprecise labels for person RE-ID

Jingmiao Zhu, Fengpeng Guo, Dongyue Chen, Shizhuo Deng

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126021J (2023) https://doi.org/10.1117/12.2668498
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

With the rise of command city construction, the importance of pedestrian re-identification based on video surveillance has become increasingly prominent. The person re-identification task mainly studies recognizing images of the same person under different cameras. Due to the high cost of labeling data in supervised learning in common methods, and the lack of guidance for labeling data in unsupervised learning, the experimental results are unsatisfactory. In this paper, we propose a new weakly supervised person re-identification problem framework: a human-computer interaction-based sample active push and auxiliary labeling method is designed to quickly obtain a small number of identity-related labels with low labor cost; At the same time, the weak supervision information that is easily obtained in the real scene is introduced into the pseudo-label generation process to improve the clustering method, aiming to solve the problem of insufficient initial labeling data of images in the label estimation stage and weakly supervised labels are not well utilized; Finally, in view of the insufficient utilization of weakly supervised labels in the model training process, a weakly supervised loss function WSP loss is designed and added to the training to optimize the overall loss function. Compared with the current state-of-the-art unsupervised and weakly supervised person re-identification work, this paper shows better performance under the same conditions and even using less labeled information.

Citation Download Citation

Jingmiao Zhu, Fengpeng Guo, Dongyue Chen, and Shizhuo Deng "A cross-domain weakly supervised method based on imprecise labels for person RE-ID", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126021J (20 April 2023); https://doi.org/10.1117/12.2668498

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Data modeling

Image processing

Cameras

Feature extraction

Performance modeling

Machine learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years