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GreyReID: A Novel Two-stream Deep Framework with RGB-grey Information for Person Re-identification

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Published:16 April 2021Publication History
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Abstract

In this article, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this article, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. First, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Second, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposed method can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance in the proposed deep framework.

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  1. GreyReID: A Novel Two-stream Deep Framework with RGB-grey Information for Person Re-identification

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 1
        February 2021
        392 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/3453992
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 April 2021
        • Accepted: 1 August 2020
        • Revised: 1 April 2020
        • Received: 1 August 2019
        Published in tomm Volume 17, Issue 1

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