14 May 2018 A sparse dimension-reduction based person re-identification algorithm
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Abstract
Focused on the issue that the person re-identification across non-overlapping camera views and the high dimensional features extracted from the images, a novel person re-identification algorithm is proposed. The algorithm obtained the semantic information of each camera view by the sparse learning, and then the Canonical Correlation Analysis (CCA) is used to carry out the high-level feature projection transformation. The algorithm aims to avoid the curse of dimensionality caused by the high dimensional feature operation via improving the feature matching ability. To the end, the characteristic distance between different views can be compared. The advantages of this method is to learn the robust pedestrian image feature representation and it also builds person re-identification model with block structure feature of pedestrian dataset, and the associated optimization problem is solved by utilizing the alternating directions framework in order to improve the performance of person re-identification. At last, the experimental results show that the proposed method has higher recognition efficiency on three benchmark datasets of the PRID 2011, iLIDS-VID and VIPeR.
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Hongyuan Wang, Hongyuan Wang, Wenwen Zhang, Wenwen Zhang, Jinyu Sun, Jinyu Sun, Lei Geng, Lei Geng, Chong Wang, Chong Wang, Jianwu Wan, Jianwu Wan, Fuhua Chen, Fuhua Chen, } "A sparse dimension-reduction based person re-identification algorithm", Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 106700O (14 May 2018); doi: 10.1117/12.2309846; https://doi.org/10.1117/12.2309846
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