Person re-identification for 365-day video surveillance based on stride convolutional neural network

Shengke Wang; Xiaoyan Zhang; Rui Li; Jianlin Zhu; Fenghui Xue; Junyu Dong

doi:10.1117/12.2524371

6 May 2019 Person re-identification for 365-day video surveillance based on stride convolutional neural network

Shengke Wang, Xiaoyan Zhang, Rui Li, Jianlin Zhu, Fenghui Xue, Junyu Dong

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690N (2019) https://doi.org/10.1117/12.2524371
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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

Citation Download Citation

Shengke Wang, Xiaoyan Zhang, Rui Li, Jianlin Zhu, Fenghui Xue, and Junyu Dong "Person re-identification for 365-day video surveillance based on stride convolutional neural network", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690N (6 May 2019); https://doi.org/10.1117/12.2524371

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KEYWORDS

Convolutional neural networks

Convolution

Cameras

Video surveillance

Data modeling

Expectation maximization algorithms

Lithium

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