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Proceedings Paper

Person re-identification for 365-day video surveillance based on stride convolutional neural network
Author(s): Shengke Wang; Xiaoyan Zhang; Rui Li; Jianlin Zhu; Fenghui Xue; Junyu Dong
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Paper Abstract

Person re-identification (ReID) is an important task in video surveillance and can be applied in various practical applications. The traditional methods and deep learning model cannot satisfy the real-world challenges of environmental complexity and scene dynamics, especially under fixed scene. What’s more, most of the existing datasets are outdoor and has a single style, which is not good for indoor person re-identification. Focusing on these problems, the paper improves a Stride Convolutional Neural Network (S-CNN) to process indoor images based on multi-features fusion. The deep model is established in which the identity information, stride information and other information are learned to handle more challenging indoor images. Then a metric learning method (Joint Bayesian) is employed based on the deep model. Finally, the entire classifier is retrained with supervised learning. The experiment is tested on the OUC365 dataset created by us which is captured for 365 days including all seasons style. Compared with other state-of-the-art methods, the performance of the proposed method yields best results

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690N (6 May 2019); doi: 10.1117/12.2524371
Show Author Affiliations
Shengke Wang, Ocean Univ. of China (China)
Xiaoyan Zhang, Ocean Univ. of China (China)
Rui Li, Ocean Univ. of China (China)
Jianlin Zhu, Ocean Univ. of China (China)
Fenghui Xue, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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