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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3419439","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T12:42:08Z","timestamp":1618576928000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["GreyReID: A Novel Two-stream Deep Framework with RGB-grey Information for Person Re-identification"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-0702","authenticated-orcid":false,"given":"Lei","family":"Qi","sequence":"first","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Huo","sequence":"additional","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghuan","family":"Shi","sequence":"additional","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00046"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.145"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.149"},{"key":"e_1_2_1_4_1","volume-title":"IEEE International Conference on Computer Vision (ICCV'19)","author":"Dai Zuozhuo","year":"2018","unstructured":"Zuozhuo Dai , Mingqiang Chen , Siyu Zhu , and Ping Tan . 2018 . Batch feature erasing for person re-identification and beyond . In IEEE International Conference on Computer Vision (ICCV'19) . Zuozhuo Dai, Mingqiang Chen, Siyu Zhu, and Ping Tan. 2018. Batch feature erasing for person re-identification and beyond. In IEEE International Conference on Computer Vision (ICCV'19)."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243316"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00374"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240550"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_11_1","unstructured":"Alexander Hermans Lucas Beyer and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv (2017).  Alexander Hermans Lucas Beyer and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv (2017)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00954"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00117"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.27"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/305"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2700762"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00431"},{"key":"e_1_2_1_19_1","volume-title":"ACM Multimedia Conference on Multimedia Conference (ACM MM\u201919)","author":"Liu Jiawei","year":"2018","unstructured":"Jiawei Liu , Zheng-Jun Zha , Hongtao Xie , Zhiwei Xiong , and Yongdong Zhang . 2018 . CANet: Contextual-attentional attribute-appearance network for person re-identification . In ACM Multimedia Conference on Multimedia Conference (ACM MM\u201919) . Jiawei Liu, Zheng-Jun Zha, Hongtao Xie, Zhiwei Xiong, and Yongdong Zhang. 2018. CANet: Contextual-attentional attribute-appearance network for person re-identification. In ACM Multimedia Conference on Multimedia Conference (ACM MM\u201919)."},{"key":"e_1_2_1_20_1","article-title":"Unsupervised joint subspace and dictionary learning for enhanced cross-domain person re-identification","volume":"12","author":"Qi Lei","year":"2018","unstructured":"Lei Qi , Jing Huo , Xiaocong Fan , Yinghuan Shi , and Yang Gao . 2018 . Unsupervised joint subspace and dictionary learning for enhanced cross-domain person re-identification . IEEE J. Select. Topics Sig. Proc. 12 , 6 (2018). Lei Qi, Jing Huo, Xiaocong Fan, Yinghuan Shi, and Yang Gao. 2018. 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