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Cross-view kernel collaborative representation classification for person re-identification

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Abstract

Currently, person re-identification (re-ID) has been applied in many public security applications. Yet owing to the big visual appearance changes of the same identity under different views, re-ID still faces many challenges. To reduce the intra-person discrepancy, extracting more power feature representations from pedestrian images is a reasonable solution. We propose a cross-view kernel collaborative representation based classification (CV-KCRC) method for person re-ID in our work. Our method aims to find more robust and discriminative feature representations that embody cross-view information to enhance the identification capability of features. We map the image features into a high dimensional feature space first and then use view-specific projection matrices to project the high dimensional features into a common low dimensional subspace. We expect that in the shared subspace the codings of same person from different views have the highest similarity and better performance can be achieved. Experiments on seven commonly used datasets reveal that our algorithm outperforms many state-of-the-art algorithms.

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  1. https://github.com/xiayang14551/Person_re-ID

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Acknowledgment

This research is supported in part by the National Natural Science Foundation of China under Grant 61806099; and by the Natural Science Foundation of Jiangsu Province of China under Grant BK20180790; and by the Natural Science Research of Jiangsu Higher Education Institutions of China under Grant 8KJB520033; and by Research Start-up Fund of NUIST under Grant 2243141701077. This research is also supported in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, in part by the Engineering Research Center of Digital Forensics, Ministry of Education.

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  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Guoqing Zhang, Tong Jiang, Junchuan Yang, Jiang Xu & Yuhui Zheng

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  1. Guoqing Zhang
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Zhang, G., Jiang, T., Yang, J. et al. Cross-view kernel collaborative representation classification for person re-identification. Multimed Tools Appl 80, 20687–20705 (2021). https://doi.org/10.1007/s11042-021-10671-z

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