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Person re-identification based on multi-scale convolutional network

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Abstract

Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recently, with the success of deep learning methods on many computer vision tasks, researchers started to put their focuses on learning high-performance features. In this paper, we propose a method by fusing features learned from a multi-scale convolutional neural network and the traditional hand-crafted features, which improves the performance significantly. The Shinpuhkan2014dataset has been selected as the training data, and we compare the proposed method on VIPeR, PRID, iLIDS and CUHK03 datasets. The experimental results show that the performance of the proposed method is superior to the current methods which have a training step on the testing sets.

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Acknowledgements

This work is supported by Scientific Research Project of Chongqing Education Commission No. KJ1729408.

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  1. Chongqing College of Electronic Engineering of Digital Media College, Chongqing, 401331, China

    XiuJie Yang

  2. Chongqing College of Electronic Engineering of General Education and International College, Chongqing, China

    Ping Chen

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  1. XiuJie Yang
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  2. Ping Chen
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Correspondence to XiuJie Yang.

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Yang, X., Chen, P. Person re-identification based on multi-scale convolutional network. Multimed Tools Appl 79, 9299–9313 (2020). https://doi.org/10.1007/s11042-019-7387-9

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