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A Siamese inception architecture network for person re-identification

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Abstract

Person re-identification is an extremely challenging problem as person’s appearance often undergoes dramatic changes due to the large variations of viewpoints, illuminations, poses, image resolutions, and cluttered backgrounds. How to extract discriminative features is one of the most critical ways to address these challenges. In this paper, we mainly focus on learning high-level features and combine the low-level, mid-level, and high-level features together to re-identify a person across different cameras. Firstly, we design a Siamese inception architecture network to automatically learn effective semantic features for person re-identification in different camera views. Furthermore, we combine multi-level features in null space with the null Foley–Sammon transform metric learning approach. In this null space, images of the same person are projected to a single point, which minimizes the intra-class scatter to the extreme and maximizes the relative inter-class separation simultaneously. Finally, comprehensive evaluations demonstrate that our approach achieves better performance on four person re-identification benchmark datasets, including Market-1501, CUHK03, PRID2011, and VIPeR.

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Acknowledgements

This work is supported by the National Key Research and Development Plan (2016YFC0801005), the Funds for Creative Research Groups of China (61421061), the NSFC-Guangdong Joint Fund (U1501254), the Beijing Training Project for the Leading Talents in S&T (ljrc 201502), the Cosponsored Project of Beijing Committee of Education.

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  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shuangqun Li & Huadong Ma

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  1. Shuangqun Li
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  2. Huadong Ma
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Correspondence to Huadong Ma.

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Li, S., Ma, H. A Siamese inception architecture network for person re-identification. Machine Vision and Applications 28, 725–736 (2017). https://doi.org/10.1007/s00138-017-0843-5

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